Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece
Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is comp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Antonopoulos, Vassilis Z [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2016 IAHS 2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Hydrological sciences journal - Abingdon, Oxon : Taylor & Francis, 1982, 61(2016), 14, Seite 2590-2599 |
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| Übergeordnetes Werk: |
volume:61 ; year:2016 ; number:14 ; pages:2590-2599 |
| Links: |
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| DOI / URN: |
10.1080/02626667.2016.1142667 |
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OLC198115843X |
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| 520 | |a Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned | ||
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| 650 | 4 | |a energy budget | |
| 650 | 4 | |a evaporation | |
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| 650 | 4 | |a comparison of methods | |
| 650 | 4 | |a artificial neural networks | |
| 650 | 4 | |a Lake Vegoritis | |
| 700 | 1 | |a Gianniou, Soultana K |4 oth | |
| 700 | 1 | |a Antonopoulos, Athanasios V |4 oth | |
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10.1080/02626667.2016.1142667 doi PQ20161012 (DE-627)OLC198115843X (DE-599)GBVOLC198115843X (PRQ)c1541-8ceea19a5b1a50e4c33e3e9654e90161c6dc7974abc41681cce327cec367dda40 (KEY)0010489220160000061001402590artificialneuralnetworksandempiricalequationstoest DE-627 ger DE-627 rakwb eng 550 DNB 38.85 bkl Antonopoulos, Vassilis Z verfasserin aut Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned Nutzungsrecht: © 2016 IAHS 2016 energy budget evaporation lake comparison of methods artificial neural networks Lake Vegoritis Gianniou, Soultana K oth Antonopoulos, Athanasios V oth Enthalten in Hydrological sciences journal Abingdon, Oxon : Taylor & Francis, 1982 61(2016), 14, Seite 2590-2599 (DE-627)130415235 (DE-600)625713-6 (DE-576)015917908 0262-6667 nnns volume:61 year:2016 number:14 pages:2590-2599 http://dx.doi.org/10.1080/02626667.2016.1142667 Volltext http://www.tandfonline.com/doi/abs/10.1080/02626667.2016.1142667 http://search.proquest.com/docview/1823549680 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4305 38.85 AVZ AR 61 2016 14 2590-2599 |
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10.1080/02626667.2016.1142667 doi PQ20161012 (DE-627)OLC198115843X (DE-599)GBVOLC198115843X (PRQ)c1541-8ceea19a5b1a50e4c33e3e9654e90161c6dc7974abc41681cce327cec367dda40 (KEY)0010489220160000061001402590artificialneuralnetworksandempiricalequationstoest DE-627 ger DE-627 rakwb eng 550 DNB 38.85 bkl Antonopoulos, Vassilis Z verfasserin aut Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned Nutzungsrecht: © 2016 IAHS 2016 energy budget evaporation lake comparison of methods artificial neural networks Lake Vegoritis Gianniou, Soultana K oth Antonopoulos, Athanasios V oth Enthalten in Hydrological sciences journal Abingdon, Oxon : Taylor & Francis, 1982 61(2016), 14, Seite 2590-2599 (DE-627)130415235 (DE-600)625713-6 (DE-576)015917908 0262-6667 nnns volume:61 year:2016 number:14 pages:2590-2599 http://dx.doi.org/10.1080/02626667.2016.1142667 Volltext http://www.tandfonline.com/doi/abs/10.1080/02626667.2016.1142667 http://search.proquest.com/docview/1823549680 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4305 38.85 AVZ AR 61 2016 14 2590-2599 |
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10.1080/02626667.2016.1142667 doi PQ20161012 (DE-627)OLC198115843X (DE-599)GBVOLC198115843X (PRQ)c1541-8ceea19a5b1a50e4c33e3e9654e90161c6dc7974abc41681cce327cec367dda40 (KEY)0010489220160000061001402590artificialneuralnetworksandempiricalequationstoest DE-627 ger DE-627 rakwb eng 550 DNB 38.85 bkl Antonopoulos, Vassilis Z verfasserin aut Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned Nutzungsrecht: © 2016 IAHS 2016 energy budget evaporation lake comparison of methods artificial neural networks Lake Vegoritis Gianniou, Soultana K oth Antonopoulos, Athanasios V oth Enthalten in Hydrological sciences journal Abingdon, Oxon : Taylor & Francis, 1982 61(2016), 14, Seite 2590-2599 (DE-627)130415235 (DE-600)625713-6 (DE-576)015917908 0262-6667 nnns volume:61 year:2016 number:14 pages:2590-2599 http://dx.doi.org/10.1080/02626667.2016.1142667 Volltext http://www.tandfonline.com/doi/abs/10.1080/02626667.2016.1142667 http://search.proquest.com/docview/1823549680 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4305 38.85 AVZ AR 61 2016 14 2590-2599 |
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10.1080/02626667.2016.1142667 doi PQ20161012 (DE-627)OLC198115843X (DE-599)GBVOLC198115843X (PRQ)c1541-8ceea19a5b1a50e4c33e3e9654e90161c6dc7974abc41681cce327cec367dda40 (KEY)0010489220160000061001402590artificialneuralnetworksandempiricalequationstoest DE-627 ger DE-627 rakwb eng 550 DNB 38.85 bkl Antonopoulos, Vassilis Z verfasserin aut Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned Nutzungsrecht: © 2016 IAHS 2016 energy budget evaporation lake comparison of methods artificial neural networks Lake Vegoritis Gianniou, Soultana K oth Antonopoulos, Athanasios V oth Enthalten in Hydrological sciences journal Abingdon, Oxon : Taylor & Francis, 1982 61(2016), 14, Seite 2590-2599 (DE-627)130415235 (DE-600)625713-6 (DE-576)015917908 0262-6667 nnns volume:61 year:2016 number:14 pages:2590-2599 http://dx.doi.org/10.1080/02626667.2016.1142667 Volltext http://www.tandfonline.com/doi/abs/10.1080/02626667.2016.1142667 http://search.proquest.com/docview/1823549680 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4305 38.85 AVZ AR 61 2016 14 2590-2599 |
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10.1080/02626667.2016.1142667 doi PQ20161012 (DE-627)OLC198115843X (DE-599)GBVOLC198115843X (PRQ)c1541-8ceea19a5b1a50e4c33e3e9654e90161c6dc7974abc41681cce327cec367dda40 (KEY)0010489220160000061001402590artificialneuralnetworksandempiricalequationstoest DE-627 ger DE-627 rakwb eng 550 DNB 38.85 bkl Antonopoulos, Vassilis Z verfasserin aut Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned Nutzungsrecht: © 2016 IAHS 2016 energy budget evaporation lake comparison of methods artificial neural networks Lake Vegoritis Gianniou, Soultana K oth Antonopoulos, Athanasios V oth Enthalten in Hydrological sciences journal Abingdon, Oxon : Taylor & Francis, 1982 61(2016), 14, Seite 2590-2599 (DE-627)130415235 (DE-600)625713-6 (DE-576)015917908 0262-6667 nnns volume:61 year:2016 number:14 pages:2590-2599 http://dx.doi.org/10.1080/02626667.2016.1142667 Volltext http://www.tandfonline.com/doi/abs/10.1080/02626667.2016.1142667 http://search.proquest.com/docview/1823549680 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4305 38.85 AVZ AR 61 2016 14 2590-2599 |
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Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece |
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Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece |
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Antonopoulos, Vassilis Z |
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Antonopoulos, Vassilis Z |
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artificial neural networks and empirical equations to estimate daily evaporation: application to lake vegoritis, greece |
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Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece |
| abstract |
Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned |
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Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned |
| abstract_unstemmed |
Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d −1 and correlation coefficient from 0.79 to 0.92. EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned |
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Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece |
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Gianniou, Soultana K Antonopoulos, Athanasios V |
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