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Hydrological Modelling using Process based and Data Driven Models: Process-based and Neural Network Modelling in Hydrology - Couverture souple
Synopsis
Point source pollution is relatively easily controllable and identifiable. Pollutants detected in a concentrated water source such as stream, river or lake are called non-point source pollution. Sedimentation and deteriorating water quality due to non-point source pollution are nowadays a very serious problem in reservoirs, lakes and rivers and is caused by high discharges of nutrients and sediment from upstream river basins. Minimization of the discharges and improvement of the agricultural practices are the obvious solution of the problem. The application of various hydrological models helps the planners and water resource managers to plan appropriately to implement effective measures. In the present study, process-based Soil and Water Assessment Tool (SWAT) and artificial neural network models such as Multi-Layer Perceptron (MLP) and Radial Basis Neural Network (RBNN) have been applied to simulate hydrological processes such as stream flow and sediment yield on monthly time steps in agricultural watershed in Eastern India. The model outputs are compared in terms of well laid out statistical criterion.
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Hydrological Modelling using Process based and Data Driven Models
Edité par
Scholars' Press Jan 2013, 2013
ISBN 10 : 3639511077
ISBN 13 : 9783639511079
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Point source pollution is relatively easily controllable and identifiable. Pollutants detected in a concentrated water source such as stream, river or lake are called non-point source pollution. Sedimentation and deteriorating water quality due to non-point source pollution are nowadays a very serious problem in reservoirs, lakes and rivers and is caused by high discharges of nutrients and sediment from upstream river basins. Minimization of the discharges and improvement of the agricultural practices are the obvious solution of the problem. The application of various hydrological models helps the planners and water resource managers to plan appropriately to implement effective measures. In the present study, process-based Soil and Water Assessment Tool (SWAT) and artificial neural network models such as Multi-Layer Perceptron (MLP) and Radial Basis Neural Network (RBNN) have been applied to simulate hydrological processes such as stream flow and sediment yield on monthly time steps in agricultural watershed in Eastern India. The model outputs are compared in terms of well laid out statistical criterion. 268 pp. Englisch. N° de réf. du vendeur 9783639511079
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Hydrological Modelling using Process based and Data Driven Models
Edité par
Scholars\' Press, 2013
ISBN 10 : 3639511077
ISBN 13 : 9783639511079
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh AjaiDr. Ajai Singh is working as Assistant Professor in Uttar Banga Krishi Viswavidyalaya, India. His recent published works are on Micro-Irrigation, Soil and Water Assessment Tool, and Neural Network modelling in simulation of. N° de réf. du vendeur 4992875
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Hydrological Modelling using Process based and Data Driven.
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Hydrological Modelling using Process based and Data Driven Models | Process-based and Neural Network Modelling in Hydrology
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Taschenbuch. Etat : Neu. Hydrological Modelling using Process based and Data Driven Models | Process-based and Neural Network Modelling in Hydrology | Ajai Singh (u. a.) | Taschenbuch | 268 S. | Englisch | 2013 | Scholars' Press | EAN 9783639511079 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 106079304
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Hydrological Modelling using Process based and Data Driven.
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Etat : New. Print on Demand. N° de réf. du vendeur 126706427
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Hydrological Modelling using Process based and Data Driven Models
Edité par
Scholars' Press Jan 2013, 2013
ISBN 10 : 3639511077
ISBN 13 : 9783639511079
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Point source pollution is relatively easily controllable and identifiable. Pollutants detected in a concentrated water source such as stream, river or lake are called non-point source pollution. Sedimentation and deteriorating water quality due to non-point source pollution are nowadays a very serious problem in reservoirs, lakes and rivers and is caused by high discharges of nutrients and sediment from upstream river basins. Minimization of the discharges and improvement of the agricultural practices are the obvious solution of the problem. The application of various hydrological models helps the planners and water resource managers to plan appropriately to implement effective measures. In the present study, process-based Soil and Water Assessment Tool (SWAT) and artificial neural network models such as Multi-Layer Perceptron (MLP) and Radial Basis Neural Network (RBNN) have been applied to simulate hydrological processes such as stream flow and sediment yield on monthly time steps in agricultural watershed in Eastern India. The model outputs are compared in terms of well laid out statistical criterion.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 268 pp. Englisch. N° de réf. du vendeur 9783639511079
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Hydrological Modelling using Process based and Data Driven.
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Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18133881134
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Hydrological Modelling using Process based and Data Driven Models : Process-based and Neural Network Modelling in Hydrology
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Point source pollution is relatively easily controllable and identifiable. Pollutants detected in a concentrated water source such as stream, river or lake are called non-point source pollution. Sedimentation and deteriorating water quality due to non-point source pollution are nowadays a very serious problem in reservoirs, lakes and rivers and is caused by high discharges of nutrients and sediment from upstream river basins. Minimization of the discharges and improvement of the agricultural practices are the obvious solution of the problem. The application of various hydrological models helps the planners and water resource managers to plan appropriately to implement effective measures. In the present study, process-based Soil and Water Assessment Tool (SWAT) and artificial neural network models such as Multi-Layer Perceptron (MLP) and Radial Basis Neural Network (RBNN) have been applied to simulate hydrological processes such as stream flow and sediment yield on monthly time steps in agricultural watershed in Eastern India. The model outputs are compared in terms of well laid out statistical criterion. N° de réf. du vendeur 9783639511079
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