Solving problems in environmental engineering and geosciences with artificial neural networks

Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dowla, Farid U. [verfasserIn] Rogers, Leah L. [mitwirkender]

Format:	E-Book
Sprache:	Englisch

Erschienen:	Cambridge, Massachusetts: MIT Press ; 2003

Schlagwörter:	Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing

Anmerkung:	Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015
Umfang:	1 PDF (x, 239 pages) ; illustrations, maps.
Beschreibung:	Mode of access: World Wide Web.

Links:	Link aufrufen Link aufrufen
ISBN:	978-0-262-27191-2

Katalog-ID:	1727348591

Internformat


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Indexfelder

author_variant	f u d fu fud
matchkey_str	book:9780262271912:2003----
oclc_num	1196098134
hierarchy_sort_str	[2003]
publishDate	2003
allfields	9780262271912 electronic 978-0-262-27191-2 (DE-627)1727348591 (DE-599)KEP055121578 (OCoLC)1196098134 (MITPRESS)6308075 (EBP)055121578 DE-627 ger DE-627 rda eng Dowla, Farid U. verfasserin aut Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Cambridge, Massachusetts MIT Press [2003] 1 PDF (x, 239 pages) illustrations, maps. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Mode of access: World Wide Web. Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing Rogers, Leah L. mitwirkender ctb 9780262515726 Erscheint auch als Druck-Ausgabe 9780262515726 https://ieeexplore.ieee.org/book/6308075 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 22 01 0018 3848474689 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240182 olr-MIT i z 23-07-21 100 01 3100 4472467291 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221589 olr-ebook mitieee 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. i z 01-12-21 2015 01 DE-93 3740749326 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6308075 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6308075 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6308075 100 01 3100 https://ieeexplore.ieee.org/book/6308075 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6308075 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6308075 2015 01 DE-93 https://ieeexplore.ieee.org/book/6308075 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01
spelling	9780262271912 electronic 978-0-262-27191-2 (DE-627)1727348591 (DE-599)KEP055121578 (OCoLC)1196098134 (MITPRESS)6308075 (EBP)055121578 DE-627 ger DE-627 rda eng Dowla, Farid U. verfasserin aut Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Cambridge, Massachusetts MIT Press [2003] 1 PDF (x, 239 pages) illustrations, maps. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Mode of access: World Wide Web. Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing Rogers, Leah L. mitwirkender ctb 9780262515726 Erscheint auch als Druck-Ausgabe 9780262515726 https://ieeexplore.ieee.org/book/6308075 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 22 01 0018 3848474689 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240182 olr-MIT i z 23-07-21 100 01 3100 4472467291 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221589 olr-ebook mitieee 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. i z 01-12-21 2015 01 DE-93 3740749326 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6308075 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6308075 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6308075 100 01 3100 https://ieeexplore.ieee.org/book/6308075 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6308075 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6308075 2015 01 DE-93 https://ieeexplore.ieee.org/book/6308075 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01
allfields_unstemmed	9780262271912 electronic 978-0-262-27191-2 (DE-627)1727348591 (DE-599)KEP055121578 (OCoLC)1196098134 (MITPRESS)6308075 (EBP)055121578 DE-627 ger DE-627 rda eng Dowla, Farid U. verfasserin aut Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Cambridge, Massachusetts MIT Press [2003] 1 PDF (x, 239 pages) illustrations, maps. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Mode of access: World Wide Web. Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing Rogers, Leah L. mitwirkender ctb 9780262515726 Erscheint auch als Druck-Ausgabe 9780262515726 https://ieeexplore.ieee.org/book/6308075 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 22 01 0018 3848474689 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240182 olr-MIT i z 23-07-21 100 01 3100 4472467291 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221589 olr-ebook mitieee 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. i z 01-12-21 2015 01 DE-93 3740749326 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6308075 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6308075 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6308075 100 01 3100 https://ieeexplore.ieee.org/book/6308075 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6308075 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6308075 2015 01 DE-93 https://ieeexplore.ieee.org/book/6308075 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01
allfieldsGer	9780262271912 electronic 978-0-262-27191-2 (DE-627)1727348591 (DE-599)KEP055121578 (OCoLC)1196098134 (MITPRESS)6308075 (EBP)055121578 DE-627 ger DE-627 rda eng Dowla, Farid U. verfasserin aut Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Cambridge, Massachusetts MIT Press [2003] 1 PDF (x, 239 pages) illustrations, maps. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Mode of access: World Wide Web. Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing Rogers, Leah L. mitwirkender ctb 9780262515726 Erscheint auch als Druck-Ausgabe 9780262515726 https://ieeexplore.ieee.org/book/6308075 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 22 01 0018 3848474689 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240182 olr-MIT i z 23-07-21 100 01 3100 4472467291 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221589 olr-ebook mitieee 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. i z 01-12-21 2015 01 DE-93 3740749326 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6308075 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6308075 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6308075 100 01 3100 https://ieeexplore.ieee.org/book/6308075 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6308075 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6308075 2015 01 DE-93 https://ieeexplore.ieee.org/book/6308075 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01
allfieldsSound	9780262271912 electronic 978-0-262-27191-2 (DE-627)1727348591 (DE-599)KEP055121578 (OCoLC)1196098134 (MITPRESS)6308075 (EBP)055121578 DE-627 ger DE-627 rda eng Dowla, Farid U. verfasserin aut Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Cambridge, Massachusetts MIT Press [2003] 1 PDF (x, 239 pages) illustrations, maps. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Mode of access: World Wide Web. Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing Rogers, Leah L. mitwirkender ctb 9780262515726 Erscheint auch als Druck-Ausgabe 9780262515726 https://ieeexplore.ieee.org/book/6308075 X:MITPRESS Verlag lizenzpflichtig ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 22 01 0018 3848474689 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240182 olr-MIT i z 23-07-21 100 01 3100 4472467291 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221589 olr-ebook mitieee 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. i z 01-12-21 2015 01 DE-93 3740749326 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6308075 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6308075 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6308075 100 01 3100 https://ieeexplore.ieee.org/book/6308075 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6308075 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6308075 2015 01 DE-93 https://ieeexplore.ieee.org/book/6308075 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01
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topic_facet	Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing
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authorswithroles_txt_mv	Dowla, Farid U. @@aut@@ Rogers, Leah L. @@ctb@@
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author	Dowla, Farid U.
spellingShingle	Dowla, Farid U. misc Neural networks (Computer science) misc Environmental engineering ; Data processing misc Earth sciences ; Data processing Solving problems in environmental engineering and geosciences with artificial neural networks
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topic_title	Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers Neural networks (Computer science) Environmental engineering ; Data processing Earth sciences ; Data processing
publisher	MIT Press
publisherStr	MIT Press
topic	misc Neural networks (Computer science) misc Environmental engineering ; Data processing misc Earth sciences ; Data processing
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title	Solving problems in environmental engineering and geosciences with artificial neural networks
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title_full	Solving problems in environmental engineering and geosciences with artificial neural networks Farid U. Dowla and Leah L. Rogers
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title_sort	solving problems in environmental engineering and geosciences with artificial neural networks
title_auth	Solving problems in environmental engineering and geosciences with artificial neural networks
abstract	Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015
abstractGer	Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015
abstract_unstemmed	Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015
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title_short	Solving problems in environmental engineering and geosciences with artificial neural networks
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up_date	2024-07-04T22:58:48.595Z
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Solving problems in environmental engineering and geosciences with artificial neural networks

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Vorhandene Bände

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