Verlag: LAP LAMBERT Academic Publishing Jul 2016, 2016
ISBN 10: 3659925357 ISBN 13: 9783659925351
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -Simulation optimization techniques are often used to address many problems of groundwater management. In these techniques an aquifer simulation model is coupled with an optimization algorithm to get the optimal solution. Simulation model normally engages a finite difference, finite element or a mesh free technique. ANN and SVM based proxy simulators are used in this study to replace the FEM simulation model. When the objective function in the study is implicit, its gradient cannot be determined with respect to the decision variables which restrain the use of gradient based optimization methods. In this study differential evolution optimization model is used to minimize the collective drawdown for obtaining an optimal pumping policy for the aquifer system. The ANN and SVM based simulation optimization models result in noteworthy saving of computational time.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch.
Verlag: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659925357 ISBN 13: 9783659925351
Sprache: Englisch
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.
Verlag: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659925357 ISBN 13: 9783659925351
Sprache: Englisch
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Parihar Aditya Pratap SinghFirst author Aditya obtained his M.Tech. degree in Water Resources Engineering from the Department of Civil Engg. at IIT Bombay. Second author Prof Rastogi earned his Ph.D. degree in Civil Engg. from the U.
Verlag: LAP LAMBERT Academic Publishing Jul 2016, 2016
ISBN 10: 3659925357 ISBN 13: 9783659925351
Sprache: Englisch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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In den WarenkorbTaschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Simulation optimization techniques are often used to address many problems of groundwater management. In these techniques an aquifer simulation model is coupled with an optimization algorithm to get the optimal solution. Simulation model normally engages a finite difference, finite element or a mesh free technique. ANN and SVM based proxy simulators are used in this study to replace the FEM simulation model. When the objective function in the study is implicit, its gradient cannot be determined with respect to the decision variables which restrain the use of gradient based optimization methods. In this study differential evolution optimization model is used to minimize the collective drawdown for obtaining an optimal pumping policy for the aquifer system. The ANN and SVM based simulation optimization models result in noteworthy saving of computational time. 112 pp. Englisch.
Verlag: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659925357 ISBN 13: 9783659925351
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 49,90
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In den WarenkorbTaschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Simulation optimization techniques are often used to address many problems of groundwater management. In these techniques an aquifer simulation model is coupled with an optimization algorithm to get the optimal solution. Simulation model normally engages a finite difference, finite element or a mesh free technique. ANN and SVM based proxy simulators are used in this study to replace the FEM simulation model. When the objective function in the study is implicit, its gradient cannot be determined with respect to the decision variables which restrain the use of gradient based optimization methods. In this study differential evolution optimization model is used to minimize the collective drawdown for obtaining an optimal pumping policy for the aquifer system. The ANN and SVM based simulation optimization models result in noteworthy saving of computational time.