The Application of Neural Networks in the Earth System Sciences : Neural Networks Emulations for Complex Multidimensional Mappings
This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear map...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Krasnopolsky, Vladimir M. [verfasserIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Dordrecht: Springer ; 2013 |
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| Schlagwörter: |
Geowissenschaften / Neuronales Netz Geowissenschaften / Neuronales Netz / Geoinformatik / Geostatistik Fernerkundung / Klimatologie / Meteorologie / Modellierung / Neuronales Netz |
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| Schlagwörter: |
| Systematik: |
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| Anmerkung: |
Description based upon print version of record |
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| Umfang: |
Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) |
| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe Krasnopolsky, Vladimir M.: The application of neural networks in the earth system Sciences - Dordrecht : Springer, 2013 |
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| Reihe: |
Atmospheric and Oceanographic Sciences Library ; 46 SpringerLink ; Bücher |
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| Links: | |
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| ISBN: |
978-94-007-6073-8 |
| DOI / URN: |
10.1007/978-94-007-6073-8 |
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| Katalog-ID: |
1652501185 |
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| 100 | 1 | |a Krasnopolsky, Vladimir M. |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The Application of Neural Networks in the Earth System Sciences |b Neural Networks Emulations for Complex Multidimensional Mappings |c by Vladimir M. Krasnopolsky |
| 264 | 1 | |a Dordrecht |b Springer |c 2013 | |
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| 505 | 8 | 0 | |a Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity |
| 505 | 8 | 0 | |a 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique |
| 505 | 8 | 0 | |a 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications |
| 505 | 8 | 0 | |a 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms |
| 505 | 8 | 0 | |a 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization |
| 505 | 8 | 0 | |a 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics |
| 520 | |a This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... | ||
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9789400760738 978-94-007-6073-8 10.1007/978-94-007-6073-8 doi (DE-627)1652501185 (DE-576)386845166 (DE-599)BSZ386845166 (OCoLC)855544090 (DE-He213)978-94-007-6073-8 DE-627 ger DE-627 rakwb eng XA-NL QC851-999 RB bicssc SCI042000 bisacsh UT 1150 rvk (DE-625)rvk/146765: UT 6200 rvk (DE-625)rvk/146834: ST 301 rvk (DE-625)rvk/143651: RB 10103 BVB rvk (DE-625)rvk/142220:12616 RB 10239 BVB rvk (DE-625)rvk/142220:12669 Krasnopolsky, Vladimir M. verfasserin aut The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings by Vladimir M. Krasnopolsky Dordrecht Springer 2013 Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atmospheric and Oceanographic Sciences Library 46 SpringerLink Bücher Description based upon print version of record Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Geography Oceanography Remote sensing Artificial intelligence Engineering Earth Sciences Geography Oceanography Remote sensing Artificial intelligence Engineering s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd DE-101 s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd (DE-627) s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4031178-8 (DE-627)106266950 (DE-576)208990968 Klimatologie gnd s (DE-588)4038953-4 (DE-627)106230166 (DE-576)209032626 Meteorologie gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd (DE-627) 9789400760721 Erscheint auch als Druck-Ausgabe Krasnopolsky, Vladimir M. The application of neural networks in the earth system Sciences Dordrecht : Springer, 2013 XVII, 189 S. (DE-627)74755837X (DE-576)382965019 9789400760721 https://doi.org/10.1007/978-94-007-6073-8 Verlag Volltext http://dx.doi.org/10.1007/978-94-007-6073-8 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz386845166cov.jpg V:DE-576 X:springer image/jpeg 20140212094231 Cover https://swbplus.bsz-bw.de/bsz382965019inh.htm V:DE-576;B:DE-105 application/pdf 20130805133255 Inhaltsverzeichnis (DE-627)751296821 ZDB-2-EES 2013 GBV_ILN_40 ISIL_DE-7 SYSFLAG_1 GBV_KXP SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_60 ISIL_DE-705 GBV_ILN_62 ISIL_DE-28 GBV_ILN_65 ISIL_DE-3 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_120 ISIL_DE-715 GBV_ILN_285 ISIL_DE-517 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2017 ISIL_DE-576 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2050 ISIL_DE-Zi4 GBV_ILN_2061 ISIL_DE-520 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef UT 1150 Methodik, Arbeitsmittel, mathematische Verfahren Physik Physik des festen Erdkörpers - Geophysik Geophysik allgemein Methodik, Arbeitsmittel, mathematische Verfahren (DE-627)1399177834 (DE-625)rvk/146765: (DE-576)329177834 UT 6200 Allgemeine Modellierung Physik Physik und Chemie der Atmosphäre - Meteorologie Allgemeine Meteorologie Modellierung Allgemeine Modellierung (DE-627)1429862122 (DE-625)rvk/146834: (DE-576)359862128 ST 301 Soft computing, Neuronale Netze, Fuzzy-Systeme Informatik Monografien Künstliche Intelligenz Soft computing, Neuronale Netze, Fuzzy-Systeme (DE-627)1272555577 (DE-625)rvk/143651: (DE-576)202555577 RB 10103 Mathematik und Statistik (soweit geographisch) Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Mathematik und Statistik (soweit geographisch) (DE-627)1271486156 (DE-625)rvk/142220:12616 (DE-576)201486156 RB 10239 Datenverarbeitung in der Kartografie Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Mathematische Geografie und Physiogeografie Mathematische Geografie und Kartografie Kartografie Teilgebiete und Einzelfragen Datenverarbeitung in der Kartografie (DE-627)1271509210 (DE-625)rvk/142220:12669 (DE-576)201509210 BO 045F 551.5 40 01 0007 1416306684 OLR-SPRINGER-EES nl xsn 19-07-13 60 01 0705 1416299033 SpringerLink Vervielfältigungen (z.B. 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9789400760738 978-94-007-6073-8 10.1007/978-94-007-6073-8 doi (DE-627)1652501185 (DE-576)386845166 (DE-599)BSZ386845166 (OCoLC)855544090 (DE-He213)978-94-007-6073-8 DE-627 ger DE-627 rakwb eng XA-NL QC851-999 RB bicssc SCI042000 bisacsh UT 1150 rvk (DE-625)rvk/146765: UT 6200 rvk (DE-625)rvk/146834: ST 301 rvk (DE-625)rvk/143651: RB 10103 BVB rvk (DE-625)rvk/142220:12616 RB 10239 BVB rvk (DE-625)rvk/142220:12669 Krasnopolsky, Vladimir M. verfasserin aut The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings by Vladimir M. Krasnopolsky Dordrecht Springer 2013 Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atmospheric and Oceanographic Sciences Library 46 SpringerLink Bücher Description based upon print version of record Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Geography Oceanography Remote sensing Artificial intelligence Engineering Earth Sciences Geography Oceanography Remote sensing Artificial intelligence Engineering s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd DE-101 s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd (DE-627) s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4031178-8 (DE-627)106266950 (DE-576)208990968 Klimatologie gnd s (DE-588)4038953-4 (DE-627)106230166 (DE-576)209032626 Meteorologie gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd (DE-627) 9789400760721 Erscheint auch als Druck-Ausgabe Krasnopolsky, Vladimir M. The application of neural networks in the earth system Sciences Dordrecht : Springer, 2013 XVII, 189 S. (DE-627)74755837X (DE-576)382965019 9789400760721 https://doi.org/10.1007/978-94-007-6073-8 Verlag Volltext http://dx.doi.org/10.1007/978-94-007-6073-8 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz386845166cov.jpg V:DE-576 X:springer image/jpeg 20140212094231 Cover https://swbplus.bsz-bw.de/bsz382965019inh.htm V:DE-576;B:DE-105 application/pdf 20130805133255 Inhaltsverzeichnis (DE-627)751296821 ZDB-2-EES 2013 GBV_ILN_40 ISIL_DE-7 SYSFLAG_1 GBV_KXP SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_60 ISIL_DE-705 GBV_ILN_62 ISIL_DE-28 GBV_ILN_65 ISIL_DE-3 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_120 ISIL_DE-715 GBV_ILN_285 ISIL_DE-517 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2017 ISIL_DE-576 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2050 ISIL_DE-Zi4 GBV_ILN_2061 ISIL_DE-520 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef UT 1150 Methodik, Arbeitsmittel, mathematische Verfahren Physik Physik des festen Erdkörpers - Geophysik Geophysik allgemein Methodik, Arbeitsmittel, mathematische Verfahren (DE-627)1399177834 (DE-625)rvk/146765: (DE-576)329177834 UT 6200 Allgemeine Modellierung Physik Physik und Chemie der Atmosphäre - Meteorologie Allgemeine Meteorologie Modellierung Allgemeine Modellierung (DE-627)1429862122 (DE-625)rvk/146834: (DE-576)359862128 ST 301 Soft computing, Neuronale Netze, Fuzzy-Systeme Informatik Monografien Künstliche Intelligenz Soft computing, Neuronale Netze, Fuzzy-Systeme (DE-627)1272555577 (DE-625)rvk/143651: (DE-576)202555577 RB 10103 Mathematik und Statistik (soweit geographisch) Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Mathematik und Statistik (soweit geographisch) (DE-627)1271486156 (DE-625)rvk/142220:12616 (DE-576)201486156 RB 10239 Datenverarbeitung in der Kartografie Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Mathematische Geografie und Physiogeografie Mathematische Geografie und Kartografie Kartografie Teilgebiete und Einzelfragen Datenverarbeitung in der Kartografie (DE-627)1271509210 (DE-625)rvk/142220:12669 (DE-576)201509210 BO 045F 551.5 40 01 0007 1416306684 OLR-SPRINGER-EES nl xsn 19-07-13 60 01 0705 1416299033 SpringerLink Vervielfältigungen (z.B. 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9789400760738 978-94-007-6073-8 10.1007/978-94-007-6073-8 doi (DE-627)1652501185 (DE-576)386845166 (DE-599)BSZ386845166 (OCoLC)855544090 (DE-He213)978-94-007-6073-8 DE-627 ger DE-627 rakwb eng XA-NL QC851-999 RB bicssc SCI042000 bisacsh UT 1150 rvk (DE-625)rvk/146765: UT 6200 rvk (DE-625)rvk/146834: ST 301 rvk (DE-625)rvk/143651: RB 10103 BVB rvk (DE-625)rvk/142220:12616 RB 10239 BVB rvk (DE-625)rvk/142220:12669 Krasnopolsky, Vladimir M. verfasserin aut The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings by Vladimir M. Krasnopolsky Dordrecht Springer 2013 Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atmospheric and Oceanographic Sciences Library 46 SpringerLink Bücher Description based upon print version of record Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... 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The application of neural networks in the earth system Sciences Dordrecht : Springer, 2013 XVII, 189 S. (DE-627)74755837X (DE-576)382965019 9789400760721 https://doi.org/10.1007/978-94-007-6073-8 Verlag Volltext http://dx.doi.org/10.1007/978-94-007-6073-8 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz386845166cov.jpg V:DE-576 X:springer image/jpeg 20140212094231 Cover https://swbplus.bsz-bw.de/bsz382965019inh.htm V:DE-576;B:DE-105 application/pdf 20130805133255 Inhaltsverzeichnis (DE-627)751296821 ZDB-2-EES 2013 GBV_ILN_40 ISIL_DE-7 SYSFLAG_1 GBV_KXP SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_60 ISIL_DE-705 GBV_ILN_62 ISIL_DE-28 GBV_ILN_65 ISIL_DE-3 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_120 ISIL_DE-715 GBV_ILN_285 ISIL_DE-517 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2017 ISIL_DE-576 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2050 ISIL_DE-Zi4 GBV_ILN_2061 ISIL_DE-520 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef UT 1150 Methodik, Arbeitsmittel, mathematische Verfahren Physik Physik des festen Erdkörpers - Geophysik Geophysik allgemein Methodik, Arbeitsmittel, mathematische Verfahren (DE-627)1399177834 (DE-625)rvk/146765: (DE-576)329177834 UT 6200 Allgemeine Modellierung Physik Physik und Chemie der Atmosphäre - Meteorologie Allgemeine Meteorologie Modellierung Allgemeine Modellierung (DE-627)1429862122 (DE-625)rvk/146834: (DE-576)359862128 ST 301 Soft computing, Neuronale Netze, Fuzzy-Systeme Informatik Monografien Künstliche Intelligenz Soft computing, Neuronale Netze, Fuzzy-Systeme (DE-627)1272555577 (DE-625)rvk/143651: (DE-576)202555577 RB 10103 Mathematik und Statistik (soweit geographisch) Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Mathematik und Statistik (soweit geographisch) (DE-627)1271486156 (DE-625)rvk/142220:12616 (DE-576)201486156 RB 10239 Datenverarbeitung in der Kartografie Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Mathematische Geografie und Physiogeografie Mathematische Geografie und Kartografie Kartografie Teilgebiete und Einzelfragen Datenverarbeitung in der Kartografie (DE-627)1271509210 (DE-625)rvk/142220:12669 (DE-576)201509210 BO 045F 551.5 40 01 0007 1416306684 OLR-SPRINGER-EES nl xsn 19-07-13 60 01 0705 1416299033 SpringerLink Vervielfältigungen (z.B. 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Zugriff von außerhalb nur für HCU-Angehörige möglich https://doi.org/10.1007/978-94-007-6073-8 2050 01 DE-Zi4 http://dx.doi.org/10.1007/978-94-007-6073-8 2061 01 DE-520 http://dx.doi.org/10.1007/978-94-007-6073-8 2148 01 DE-950 http://dx.doi.org/10.1007/978-94-007-6073-8 2027 01 DE-105 00 s ebook 40 00 DE-7 00 (DE-627)625047648 VBD 000 Mathematische und Statistische Geologie 40 00 DE-7 01 (DE-627)623607255 TUA 300 Mathematische und EDV-Verfahren {Meteorologie} 40 00 DE-7 02 (DE-627)623604558 TM 300 Methodik, Arbeitsmittel, Abkürzungsverzeichnisse {Geophysik} 120 00 DE-715 99 ww 285 00 DE-517 00 ST 301 285 00 DE-517 00 UT 1150 285 00 DE-517 00 UT 6200 2027 01 DE-105 00 (DE-627)1293812447 DK 55.001.57 2027 01 DE-105 00 (DE-627)1292244216 DK 550.8.05 2027 01 DE-105 00 (DE-627)1301549673 DK 551.501.4 2027 01 DE-105 00 (DE-627)1292776218 DK 551.5 2027 01 DE-105 00 (DE-627)1292192038 DK 519.711 2027 01 DE-105 00 (DE-627)1292433272 DK 528.8 2027 01 DE-105 00 (DE-627)1293391980 DK 51-7 120 01 0715 24619808 120 01 0715 YH 2020 40 01 0007 OLR-SPRINGER-EES 60 01 0705 SpringerLink 62 01 0028 OLR-EES 65 01 0003 OLR-SEB-ZDB-2-EES 110 01 3110 OLR-SEB 120 01 0715 OLR-ESP 120 02 0715 alma 120 03 0715 alma 285 01 0517 OLR-ESP-EES 370 01 4370 olr-springer |
| allfieldsGer |
9789400760738 978-94-007-6073-8 10.1007/978-94-007-6073-8 doi (DE-627)1652501185 (DE-576)386845166 (DE-599)BSZ386845166 (OCoLC)855544090 (DE-He213)978-94-007-6073-8 DE-627 ger DE-627 rakwb eng XA-NL QC851-999 RB bicssc SCI042000 bisacsh UT 1150 rvk (DE-625)rvk/146765: UT 6200 rvk (DE-625)rvk/146834: ST 301 rvk (DE-625)rvk/143651: RB 10103 BVB rvk (DE-625)rvk/142220:12616 RB 10239 BVB rvk (DE-625)rvk/142220:12669 Krasnopolsky, Vladimir M. verfasserin aut The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings by Vladimir M. Krasnopolsky Dordrecht Springer 2013 Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atmospheric and Oceanographic Sciences Library 46 SpringerLink Bücher Description based upon print version of record Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Geography Oceanography Remote sensing Artificial intelligence Engineering Earth Sciences Geography Oceanography Remote sensing Artificial intelligence Engineering s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd DE-101 s (DE-588)4020288-4 (DE-627)106318632 (DE-576)208932682 Geowissenschaften gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd (DE-627) s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4031178-8 (DE-627)106266950 (DE-576)208990968 Klimatologie gnd s (DE-588)4038953-4 (DE-627)106230166 (DE-576)209032626 Meteorologie gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd (DE-627) 9789400760721 Erscheint auch als Druck-Ausgabe Krasnopolsky, Vladimir M. The application of neural networks in the earth system Sciences Dordrecht : Springer, 2013 XVII, 189 S. (DE-627)74755837X (DE-576)382965019 9789400760721 https://doi.org/10.1007/978-94-007-6073-8 Verlag Volltext http://dx.doi.org/10.1007/978-94-007-6073-8 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz386845166cov.jpg V:DE-576 X:springer image/jpeg 20140212094231 Cover https://swbplus.bsz-bw.de/bsz382965019inh.htm V:DE-576;B:DE-105 application/pdf 20130805133255 Inhaltsverzeichnis (DE-627)751296821 ZDB-2-EES 2013 GBV_ILN_40 ISIL_DE-7 SYSFLAG_1 GBV_KXP SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_60 ISIL_DE-705 GBV_ILN_62 ISIL_DE-28 GBV_ILN_65 ISIL_DE-3 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_120 ISIL_DE-715 GBV_ILN_285 ISIL_DE-517 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2017 ISIL_DE-576 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2050 ISIL_DE-Zi4 GBV_ILN_2061 ISIL_DE-520 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef UT 1150 Methodik, Arbeitsmittel, mathematische Verfahren Physik Physik des festen Erdkörpers - Geophysik Geophysik allgemein Methodik, Arbeitsmittel, mathematische Verfahren (DE-627)1399177834 (DE-625)rvk/146765: (DE-576)329177834 UT 6200 Allgemeine Modellierung Physik Physik und Chemie der Atmosphäre - Meteorologie Allgemeine Meteorologie Modellierung Allgemeine Modellierung (DE-627)1429862122 (DE-625)rvk/146834: (DE-576)359862128 ST 301 Soft computing, Neuronale Netze, Fuzzy-Systeme Informatik Monografien Künstliche Intelligenz Soft computing, Neuronale Netze, Fuzzy-Systeme (DE-627)1272555577 (DE-625)rvk/143651: (DE-576)202555577 RB 10103 Mathematik und Statistik (soweit geographisch) Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Mathematik und Statistik (soweit geographisch) (DE-627)1271486156 (DE-625)rvk/142220:12616 (DE-576)201486156 RB 10239 Datenverarbeitung in der Kartografie Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Mathematische Geografie und Physiogeografie Mathematische Geografie und Kartografie Kartografie Teilgebiete und Einzelfragen Datenverarbeitung in der Kartografie (DE-627)1271509210 (DE-625)rvk/142220:12669 (DE-576)201509210 BO 045F 551.5 40 01 0007 1416306684 OLR-SPRINGER-EES nl xsn 19-07-13 60 01 0705 1416299033 SpringerLink Vervielfältigungen (z.B. 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Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. 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Zugriff von außerhalb nur für HCU-Angehörige möglich https://doi.org/10.1007/978-94-007-6073-8 2050 01 DE-Zi4 http://dx.doi.org/10.1007/978-94-007-6073-8 2061 01 DE-520 http://dx.doi.org/10.1007/978-94-007-6073-8 2148 01 DE-950 http://dx.doi.org/10.1007/978-94-007-6073-8 2027 01 DE-105 00 s ebook 40 00 DE-7 00 (DE-627)625047648 VBD 000 Mathematische und Statistische Geologie 40 00 DE-7 01 (DE-627)623607255 TUA 300 Mathematische und EDV-Verfahren {Meteorologie} 40 00 DE-7 02 (DE-627)623604558 TM 300 Methodik, Arbeitsmittel, Abkürzungsverzeichnisse {Geophysik} 120 00 DE-715 99 ww 285 00 DE-517 00 ST 301 285 00 DE-517 00 UT 1150 285 00 DE-517 00 UT 6200 2027 01 DE-105 00 (DE-627)1293812447 DK 55.001.57 2027 01 DE-105 00 (DE-627)1292244216 DK 550.8.05 2027 01 DE-105 00 (DE-627)1301549673 DK 551.501.4 2027 01 DE-105 00 (DE-627)1292776218 DK 551.5 2027 01 DE-105 00 (DE-627)1292192038 DK 519.711 2027 01 DE-105 00 (DE-627)1292433272 DK 528.8 2027 01 DE-105 00 (DE-627)1293391980 DK 51-7 120 01 0715 24619808 120 01 0715 YH 2020 40 01 0007 OLR-SPRINGER-EES 60 01 0705 SpringerLink 62 01 0028 OLR-EES 65 01 0003 OLR-SEB-ZDB-2-EES 110 01 3110 OLR-SEB 120 01 0715 OLR-ESP 120 02 0715 alma 120 03 0715 alma 285 01 0517 OLR-ESP-EES 370 01 4370 olr-springer |
| allfieldsSound |
9789400760738 978-94-007-6073-8 10.1007/978-94-007-6073-8 doi (DE-627)1652501185 (DE-576)386845166 (DE-599)BSZ386845166 (OCoLC)855544090 (DE-He213)978-94-007-6073-8 DE-627 ger DE-627 rakwb eng XA-NL QC851-999 RB bicssc SCI042000 bisacsh UT 1150 rvk (DE-625)rvk/146765: UT 6200 rvk (DE-625)rvk/146834: ST 301 rvk (DE-625)rvk/143651: RB 10103 BVB rvk (DE-625)rvk/142220:12616 RB 10239 BVB rvk (DE-625)rvk/142220:12669 Krasnopolsky, Vladimir M. verfasserin aut The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings by Vladimir M. Krasnopolsky Dordrecht Springer 2013 Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Atmospheric and Oceanographic Sciences Library 46 SpringerLink Bücher Description based upon print version of record Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... 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The application of neural networks in the earth system Sciences Dordrecht : Springer, 2013 XVII, 189 S. 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Krasnopolsky, Vladimir M. Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics misc QC851-999 rvk UT 1150 rvk UT 6200 rvk ST 301 rvk RB 10103 rvk RB 10239 misc Geography misc Oceanography misc Remote sensing misc Artificial intelligence misc Engineering misc Earth Sciences gnd Geowissenschaften gnd Neuronales Netz gnd Geoinformatik gnd Geostatistik gnd Fernerkundung gnd Klimatologie gnd Meteorologie gnd Modellierung 2027 ebook The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings |
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Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity 2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique 2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications 3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms 3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization 4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics |
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The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings |
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This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Description based upon print version of record |
| abstractGer |
This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Description based upon print version of record |
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This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promi ... Description based upon print version of record |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">1652501185</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240706232411.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">130705s2013 ne |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9789400760738</subfield><subfield code="9">978-94-007-6073-8</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-94-007-6073-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1652501185</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-576)386845166</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BSZ386845166</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)855544090</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)978-94-007-6073-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-NL</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">RB</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">SCI042000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">UT 1150</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/146765:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">UT 6200</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/146834:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/143651:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10103</subfield><subfield code="q">BVB</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/142220:12616</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10239</subfield><subfield code="q">BVB</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/142220:12669</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Krasnopolsky, Vladimir M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The Application of Neural Networks in the Earth System Sciences</subfield><subfield code="b">Neural Networks Emulations for Complex Multidimensional Mappings</subfield><subfield code="c">by Vladimir M. Krasnopolsky</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Dordrecht</subfield><subfield code="b">Springer</subfield><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Atmospheric and Oceanographic Sciences Library</subfield><subfield code="v">46</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">SpringerLink</subfield><subfield code="a">Bücher</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) 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Contents … Introduction … Systems, Subsystems, Organization, and Structure … . … Evolution of Approaches to Earth System … Neural Networks in Earth System Sciences … References … Introduction to Mapping and Neural Networks … Mapping Examples … Prediction of Time Series … Lookup Tables … Satellite Remote Sensing … Emulation of Subsystems of the Climate System … Some Generic Properties of Mappings … Mapping Dimensionalities, Domain, and Range … Mapping Complexity … Mappings Corresponding to Ill-Posed Problems … Stochastic Mappings … MLP NN: A Generic Tool for Modeling Nonlinear Mappings … NNs in Terms of Approximation Theory … NNs in Their Traditional Terms … Training Set … Selection of the NN Architecture … Normalization of the NN Inputs and Outputs … Constant Inputs and Outputs … NN Training … Advantages and Limitations of the NN Technique … Flexibility of the MLP NN … NN Training, Nonlinear Optimization, and Mulli-collinearity of Inputs and Outputs … NN Generalization: Interpolation and Extrapolation … NN Jacobian … Contents … Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches … NN Ensemble as Emulation of Stochastic Mappings … Estimates of NN Parameters' Uncertainty … NNs Versus Physically Based Models: NN as a "Black Box" … NN Emulations … Final Remarks … References … Atmospheric and Oceanic Remote Sensing Applications … Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals … Conventional P2P Retrievals … Variational Retrievals Through the Direct Assimilation of Satellite Measurements … NNs for Emulating Forward Models … NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms … Controlling the NN Generalization and Quality Control of Retrievals … Neural Network Emulations for SSM/I Data … NN Emulations for the Empirical FM for the SSM/ … NN Empirical SSM/ … Retrieval Algorithms … Controlling the NN Generalization for the SSM/I … Using NNs to Go Beyond the Standard Retrieval Paradigm … Point-Wise Retrievals … Problems with Point-Wise Retrievals … Field-Wise Retrieval Paradigms … Discussion … References … Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather … Numerical Modeling Background … Climate- and Weather-Related Numerical Models and Prediction Systems … Representation of Physics in Global and Regional Models: Para met erizations of Physics … An Example: Parameterization of Long-Wave Radiation Physics … Methods Currently Used to Reduce Computational Burden … Hybrid Model Component and a Hybrid Model … Hybrid Parameterizations of Physics … Hybrid Numerical Models … Atmospheric NN Applications … NN Emulation of Model Physics Components … Generating the Training Set … Contents xiii … NN Emulations for the Model Radiation … Validation of NN Emulations in Parallel Decadal Climate Simulations and Weather Forecasts … Compound Parameterization for NCAR CAM Short-Wave Radiation … NN-Based Convection Parameterization for NCAR CAM Derived from CRM-Simuiated Data … An Ocean Application of the Hybrid Model Approach: Neural Network Emulation of Nonlinear Interactions in Wind Wave Models … -: … NN Emulation for 5n! … Validation of NNIAE in the Model and Compound Parameterization for S^i … Discussion … Summary and Advantages of the Hybrid Modeling Approach … Limitations of the Current Hybrid Modeling Framework and Possible Solutions … References … NN Ensembles and Their Applications … Using NN Emulations of Dependencies Between Model Variables in DAS … SSH Mapping and Its NN Emulation … NN Ensembles for Improving NN Observation Operator Accuracies and Reducing NN Jacobian Uncertainties … Discussion … NN Nonlinear Multi-model Hnsembles … Calculation of the Ensemble Average … Results … Discussion … Perturbed Physics and Ensembles with Perturbed Physics … Ensemble Approaches in NWP and Climate Simulations … Neural Network Ensembles with Perturbed Physics … Comparisons of Different Ensembles with Perturbed NCAR CAM LWR … Discussion … References … Conclusions … Comments About NN Technique … Comments About Other Statistical Learning Techniques … References … Index … |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">1652501185</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240706232411.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">130705s2013 ne |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9789400760738</subfield><subfield code="9">978-94-007-6073-8</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-94-007-6073-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1652501185</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-576)386845166</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BSZ386845166</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)855544090</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)978-94-007-6073-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-NL</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">RB</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">SCI042000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">UT 1150</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/146765:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">UT 6200</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/146834:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/143651:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10103</subfield><subfield code="q">BVB</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/142220:12616</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10239</subfield><subfield code="q">BVB</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/142220:12669</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Krasnopolsky, Vladimir M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The Application of Neural Networks in the Earth System Sciences</subfield><subfield code="b">Neural Networks Emulations for Complex Multidimensional Mappings</subfield><subfield code="c">by Vladimir M. Krasnopolsky</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Dordrecht</subfield><subfield code="b">Springer</subfield><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource (XVII, 189 p. 59 illus., 24 illus. in color, digital)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Atmospheric and Oceanographic Sciences Library</subfield><subfield code="v">46</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">SpringerLink</subfield><subfield code="a">Bücher</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">Preface; Acknowledgments; Contents; Abbreviations; Chapter 1: Introduction; 1.1 Systems, Subsystems, Organization, and Structure; 1.2 Evolution of Approaches to Earth System; 1.3 Neural Networks in Earth System Sciences; References; Chapter 2: Introduction to Mapping and Neural Networks; 2.1 Mapping Examples; 2.1.1 Prediction of Time Series; 2.1.2 Lookup Tables; 2.1.3 Satellite Remote Sensing; 2.1.4 Emulation of Subsystems of the Climate System; 2.2 Some Generic Properties of Mappings; 2.2.1 Mapping Dimensionalities, Domain, and Range; 2.2.2 Mapping Complexity</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">2.2.3 Mappings Corresponding to Ill-Posed Problems2.2.4 Stochastic Mappings; 2.3 MLP NN: A Generic Tool for Modeling Nonlinear Mappings; 2.3.1 NNs in Terms of Approximation Theory; 2.3.2 NNs in Their Traditional Terms; 2.3.3 Training Set; 2.3.4 Selection of the NN Architecture; 2.3.5 Normalization of the NN Inputs and Outputs; 2.3.6 Constant Inputs and Outputs; 2.3.7 NN Training; Batch Training and Sequential Training; Missed Inputs and Outputs; Overfitting and Regularization; Noisy Training Data and Stochastic Mappings; 2.4 Advantages and Limitations of the NN Technique</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">2.4.1 Flexibility of the MLP NN2.4.2 NN Training, Nonlinear Optimization, and Multi-collinearity of Inputs and Outputs; 2.4.3 NN Generalization: Interpolation and Extrapolation; 2.4.4 NN Jacobian; 2.4.5 Multiple NN Emulations for the Same Target Mapping and NN Ensemble Approaches; 2.4.6 NN Ensemble as Emulation of Stochastic Mappings; 2.4.7 Estimates of NN Parameters' Uncertainty; 2.4.8 NNs Versus Physically Based Models: NN as a "Black Box"; 2.5 NN Emulations; 2.6 Final Remarks; References; Chapter 3: Atmospheric and Oceanic Remote Sensing Applications</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">3.1 Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals3.1.1 Conventional P2P Retrievals; 3.1.2 Variational Retrievals Through the Direct Assimilation of Satellite Measurements; 3.2 NNs for Emulating Forward Models; 3.3 NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms; 3.4 Controlling the NN Generalization and Quality Control of Retrievals; 3.5 Neural Network Emulations for SSM/I Data; 3.5.1 NN Emulations for the Empirical FM for the SSM/I; 3.5.2 NN Empirical SSM/I Retrieval Algorithms</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">3.5.3 Controlling the NN Generalization for the SSM/I3.6 Using NNs to Go Beyond the Standard Retrieval Paradigm; 3.6.1 Point-Wise Retrievals; 3.6.2 Problems with Point-Wise Retrievals; 3.6.3 Field-Wise Retrieval Paradigms; 3.7 Discussion; References; Chapter 4: Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather; 4.1 Numerical Modeling Background; 4.1.1 Climate- and Weather-Related Numerical Models and Prediction Systems; Global Models; Regional Models; Cloud-Resolving Models; Multiscale Modeling Framework or Superparameterization</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">4.1.2 Representation of Physics in Global and Regional Models: Parameterizations of Physics</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN - the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. Dr. Vladimir Krasnopolsky holds a MSc and a PhD in Physics obtained from the Moscow State University. After graduating, he has worked there as a Senior Research Scientist at the Institute of Nuclear Physics, before becoming a Physical Scientist at the NCEP/NWS/NOAA as well as an Adjunct Professor at the Earth System Science Interdisciplinary Center of the University of Maryland. Dr. Krasnopolsky is a member (former Chair) of American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science and a member of IEEE/CSI/INNS Working Group (Task Force) on Computational Intelligence in Earth and Environmental Sciences. Dr. Krasnopolsky has published over a hundred papers in scientific journals and a book on quantum mechanics. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (..) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (..) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (..) 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Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="k">Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung</subfield><subfield code="y">z</subfield><subfield code="z">19-07-13</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">62</subfield><subfield code="1">01</subfield><subfield code="x">0028</subfield><subfield code="b">1416465898</subfield><subfield code="h">OLR-EES</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">19-07-13</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">65</subfield><subfield code="1">01</subfield><subfield code="x">0003</subfield><subfield code="b">1655671707</subfield><subfield code="c">03</subfield><subfield code="f">--%%--</subfield><subfield code="d">ebook</subfield><subfield code="e">--%%--</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-SEB-ZDB-2-EES</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">k3o</subfield><subfield code="z">02-01-17</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="b">4305022168</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="h">OLR-SEB</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="y">z</subfield><subfield code="z">07-04-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">120</subfield><subfield code="1">01</subfield><subfield code="x">0715</subfield><subfield code="b">1416457585</subfield><subfield code="h">OLR-ESP</subfield><subfield code="k">Campusweiter Zugriff (Universität Oldenburg). - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. 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