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Rainfall-Runoff Modeling Using Artificial Neural Networks: Rainfall-Runoff Modeling Using Artificial Neural Networks(ANNs) and Physically-based Model-Theory Simulation and Results - Softcover
Synopsis
The book addresses a two-pronged approach for the determination of a watershed's response by developing a physically-based model and a neural network-based model. For the physically-based model, the watershed is partitioned into a series of one-dimensional overland flow planes and channel elements, and water is routed over these elements in a cascading fashion. A system of partial differential equations under the kinematic wave approximation was used to describe surface water movement. The applicability of ANNs was investigated by developing a neural network-based runoff predictive model. The performance of ANNs, with different architectures, was evaluated using monthly precipitation and temperature data (input) and watershed runoff (output) for 3 medium-sized watersheds ? El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfall-runoff models. The advantage of ANNs over the physically-based models is that they require only input and output data for mapping of an unknown function such as rainfall-runoff relationship. In the case of physically-based models a lot more data is required.
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About the Author
Dr. Jagadeesh Anmala, PhD: Obtained B.Tech, MS, PhD from IIT Bombay (Mumbai), Kansas State University, Georgia Institute of Technology. He is currently working as Assistant Professor at Birla Institute of Technology and Sciences, Pilani, Hyderabad Campus.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2010
- ISBN 10 3838383397
- ISBN 13 9783838383392
- BindingPaperback
- LanguageEnglish
- Number of pages200
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Rainfall-Runoff Modeling Using Artificial Neural Networks
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LAP LAMBERT Academic Publishing Jul 2010, 2010
ISBN 10: 3838383397
ISBN 13: 9783838383392
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book addresses a two-pronged approach for the determination of a watershed's response by developing a physically-based model and a neural network-based model. For the physically-based model, the watershed is partitioned into a series of one-dimensional overland flow planes and channel elements, and water is routed over these elements in a cascading fashion. A system of partial differential equations under the kinematic wave approximation was used to describe surface water movement. The applicability of ANNs was investigated by developing a neural network-based runoff predictive model. The performance of ANNs, with different architectures, was evaluated using monthly precipitation and temperature data (input) and watershed runoff (output) for 3 medium-sized watersheds El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfall-runoff models. The advantage of ANNs over the physically-based models is that they require only input and output data for mapping of an unknown function such as rainfall-runoff relationship. In the case of physically-based models a lot more data is required. 200 pp. Englisch. Seller Inventory # 9783838383392
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Rainfall-Runoff Modeling Using Artificial Neural Networks : Rainfall-Runoff Modeling Using Artificial Neural Networks(ANNs) and Physically-based Model-Theory Simulation and Results
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838383397
ISBN 13: 9783838383392
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book addresses a two-pronged approach for the determination of a watershed's response by developing a physically-based model and a neural network-based model. For the physically-based model, the watershed is partitioned into a series of one-dimensional overland flow planes and channel elements, and water is routed over these elements in a cascading fashion. A system of partial differential equations under the kinematic wave approximation was used to describe surface water movement. The applicability of ANNs was investigated by developing a neural network-based runoff predictive model. The performance of ANNs, with different architectures, was evaluated using monthly precipitation and temperature data (input) and watershed runoff (output) for 3 medium-sized watersheds El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfall-runoff models. The advantage of ANNs over the physically-based models is that they require only input and output data for mapping of an unknown function such as rainfall-runoff relationship. In the case of physically-based models a lot more data is required. Seller Inventory # 9783838383392
Quantity: 1 available
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Rainfall-Runoff Modeling Using Artificial Neural Networks
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838383397
ISBN 13: 9783838383392
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Softcover
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Anmala JagadeeshDr. Jagadeesh Anmala, PhD: Obtained B.Tech, MS, PhD from IIT Bombay (Mumbai), Kansas State University, Georgia Institute of Technology. He is currently working as Assistant Professor at Birla Institute of Technology . Seller Inventory # 5418598
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Rainfall-Runoff Modeling Using Artificial Neural Networks: Rainfall-Runoff Modeling Using Artificial Neural Networks(ANNs) and Physically-based Model-Theory Simulation and Results
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838383397
ISBN 13: 9783838383392
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