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Synopsis
The advantage of statistical models of input-output type is that they can be relatively easily constructed and applied, but on the other hand the disadvantage of such models is that that they don’t reveal the inner nature of observed phenomenon. Conceptual models, which have advantage of transparent functioning, but are sometimes hard to be proven correct. Artificial intelligence offers methods of machine learning from examples, which eliminate the disadvantages of statistical as well as conceptual approaches and integrate the advantages. A comprehensive data driven modelling experiment based on regression trees is presented in this book. Regression trees have been employed on practical problem of constructing a data driven model for runoff prediction from known present and past runoff at water-level-gauges and rainfall at rain gauges within the catchment. Results based on approximation and prediction accuracy obtained from regression trees are then compared with other DDM techniques namely, artificial neural networks, Gaussian process, support vector regressions and multiple linear regressions. Book is a must read for the researchers working in the field of data-driven modelling.
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About the Author
Sohail Ahmed was born in Shikarpur, Pakistan, on January 01, 1990. After finishing high school in 2008, he studied Civil Engineering at University of Engineering and Technology Lahore. He received M.Sc in Hydro Science and Engineering from Technical University of Dresden, Germany.
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Data-Driven Modelling
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LAP Lambert Academic Publishing Sep 2017, 2017
ISBN 10: 6202016353
ISBN 13: 9786202016353
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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 advantage of statistical models of input-output type is that they can be relatively easily constructed and applied, but on the other hand the disadvantage of such models is that that they don't reveal the inner nature of observed phenomenon. Conceptual models, which have advantage of transparent functioning, but are sometimes hard to be proven correct. Artificial intelligence offers methods of machine learning from examples, which eliminate the disadvantages of statistical as well as conceptual approaches and integrate the advantages. A comprehensive data driven modelling experiment based on regression trees is presented in this book. Regression trees have been employed on practical problem of constructing a data driven model for runoff prediction from known present and past runoff at water-level-gauges and rainfall at rain gauges within the catchment. Results based on approximation and prediction accuracy obtained from regression trees are then compared with other DDM techniques namely, artificial neural networks, Gaussian process, support vector regressions and multiple linear regressions. Book is a must read for the researchers working in the field of data-driven modelling. 68 pp. Englisch. Seller Inventory # 9786202016353
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Data-Driven Modelling: Investigation of data-driven flood forecasting models
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 6202016353
ISBN 13: 9786202016353
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Seller: Revaluation Books, Exeter, United Kingdom
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Paperback. Condition: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock. Seller Inventory # zk6202016353
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Data-Driven Modelling
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 6202016353
ISBN 13: 9786202016353
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tufail Sohail AhmedSohail Ahmed was born in Shikarpur, Pakistan, on January 01, 1990. After finishing high school in 2008, he studied Civil Engineering at University of Engineering and Technology Lahore. He received M.Sc in Hydro Sci. Seller Inventory # 166521635
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Data-Driven Modelling
Published by
LAP Lambert Academic Publishing Sep 2017, 2017
ISBN 10: 6202016353
ISBN 13: 9786202016353
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Taschenbuch
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Taschenbuch. Condition: Neu. Neuware -The advantage of statistical models of input-output type is that they can be relatively easily constructed and applied, but on the other hand the disadvantage of such models is that that they don¿t reveal the inner nature of observed phenomenon. Conceptual models, which have advantage of transparent functioning, but are sometimes hard to be proven correct. Artificial intelligence offers methods of machine learning from examples, which eliminate the disadvantages of statistical as well as conceptual approaches and integrate the advantages. A comprehensive data driven modelling experiment based on regression trees is presented in this book. Regression trees have been employed on practical problem of constructing a data driven model for runoff prediction from known present and past runoff at water-level-gauges and rainfall at rain gauges within the catchment. Results based on approximation and prediction accuracy obtained from regression trees are then compared with other DDM techniques namely, artificial neural networks, Gaussian process, support vector regressions and multiple linear regressions. Book is a must read for the researchers working in the field of data-driven modelling.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch. Seller Inventory # 9786202016353
Quantity: 2 available
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Data-Driven Modelling : Investigation of data-driven flood forecasting models
Published by
LAP Lambert Academic Publishing, 2017
ISBN 10: 6202016353
ISBN 13: 9786202016353
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The advantage of statistical models of input-output type is that they can be relatively easily constructed and applied, but on the other hand the disadvantage of such models is that that they don't reveal the inner nature of observed phenomenon. Conceptual models, which have advantage of transparent functioning, but are sometimes hard to be proven correct. Artificial intelligence offers methods of machine learning from examples, which eliminate the disadvantages of statistical as well as conceptual approaches and integrate the advantages. A comprehensive data driven modelling experiment based on regression trees is presented in this book. Regression trees have been employed on practical problem of constructing a data driven model for runoff prediction from known present and past runoff at water-level-gauges and rainfall at rain gauges within the catchment. Results based on approximation and prediction accuracy obtained from regression trees are then compared with other DDM techniques namely, artificial neural networks, Gaussian process, support vector regressions and multiple linear regressions. Book is a must read for the researchers working in the field of data-driven modelling. Seller Inventory # 9786202016353
Quantity: 1 available