Groundwater Modelling: A Comparison Between Multiple Regression and Artificial Neural Network Approaches - Softcover
Synopsis
Groundwater is being exploited indiscriminately to meet our ever increasing demand of water in different parts of the world. In India, the Gangetic plane is amongst the most fertile land of the country. Over the past couple of decades, intensive growth in agriculture, industries, and human population have increased the water demand substantially. As a result severe problems of groundwater table declination have taken place causing threat to future availability of water. Keeping these in view, a study was undertaken in the Ramganga-Bahgul interbasin of Uttar Pradesh, India, to investigate the groundwater behaviour, analyse the causes behind water table declination, and estimate the stages of groundwater development. The collected field data were used to develop groundwater models using multiple regression and artificial neural network (ANN) approaches for the prediction of seasonal water table depths below ground level in the study area. The performance of both multiple regression and ANN models were compared and evaluated.
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About the Author
Dr. Rupak Sarkar (Ph.D. in Civil Engineering from Indian Institute of Technology Guwahati, India) is presently working as Assistant Professor at Faculty of Technology, Uttar Banga Krishi Viswavidyalaya, India. Dr. Sarkar is actively involved in hydrological research and he has published papers in national and international journals of high repute.
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Groundwater Modelling
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LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659259489
ISBN 13: 9783659259487
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Groundwater is being exploited indiscriminately to meet our ever increasing demand of water in different parts of the world. In India, the Gangetic plane is amongst the most fertile land of the country. Over the past couple of decades, intensive growth in agriculture, industries, and human population have increased the water demand substantially. As a result severe problems of groundwater table declination have taken place causing threat to future availability of water. Keeping these in view, a study was undertaken in the Ramganga-Bahgul interbasin of Uttar Pradesh, India, to investigate the groundwater behaviour, analyse the causes behind water table declination, and estimate the stages of groundwater development. The collected field data were used to develop groundwater models using multiple regression and artificial neural network (ANN) approaches for the prediction of seasonal water table depths below ground level in the study area. The performance of both multiple regression and ANN models were compared and evaluated. 156 pp. Englisch. Seller Inventory # 9783659259487
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Groundwater Modelling
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sarkar RupakDr. Rupak Sarkar (Ph.D. in Civil Engineering from Indian Institute of Technology Guwahati, India) is presently working as Assistant Professor at Faculty of Technology, Uttar Banga Krishi Viswavidyalaya, India. Dr. Sarkar . Seller Inventory # 5143773
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Groundwater Modelling: A Comparison Between Multiple Regression And Artificial Neural Network Approaches
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ISBN 10: 3659259489
ISBN 13: 9783659259487
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Groundwater Modelling
Published by
LAP LAMBERT Academic Publishing Sep 2012, 2012
ISBN 10: 3659259489
ISBN 13: 9783659259487
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -Groundwater is being exploited indiscriminately to meet our ever increasing demand of water in different parts of the world. In India, the Gangetic plane is amongst the most fertile land of the country. Over the past couple of decades, intensive growth in agriculture, industries, and human population have increased the water demand substantially. As a result severe problems of groundwater table declination have taken place causing threat to future availability of water. Keeping these in view, a study was undertaken in the Ramganga-Bahgul interbasin of Uttar Pradesh, India, to investigate the groundwater behaviour, analyse the causes behind water table declination, and estimate the stages of groundwater development. The collected field data were used to develop groundwater models using multiple regression and artificial neural network (ANN) approaches for the prediction of seasonal water table depths below ground level in the study area. The performance of both multiple regression and ANN models were compared and evaluated.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch. Seller Inventory # 9783659259487
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Groundwater Modelling : A Comparison Between Multiple Regression and Artificial Neural Network Approaches
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659259489
ISBN 13: 9783659259487
New
Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Groundwater is being exploited indiscriminately to meet our ever increasing demand of water in different parts of the world. In India, the Gangetic plane is amongst the most fertile land of the country. Over the past couple of decades, intensive growth in agriculture, industries, and human population have increased the water demand substantially. As a result severe problems of groundwater table declination have taken place causing threat to future availability of water. Keeping these in view, a study was undertaken in the Ramganga-Bahgul interbasin of Uttar Pradesh, India, to investigate the groundwater behaviour, analyse the causes behind water table declination, and estimate the stages of groundwater development. The collected field data were used to develop groundwater models using multiple regression and artificial neural network (ANN) approaches for the prediction of seasonal water table depths below ground level in the study area. The performance of both multiple regression and ANN models were compared and evaluated. Seller Inventory # 9783659259487