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Hydrological Data Driven Modelling - A Case Study Approach (Hardcover, 2015 ed.): Renji Remesan, Jimson Mathew Hydrological Data Driven Modelling - A Case Study Approach (Hardcover, 2015 ed.)
Renji Remesan, Jimson Mathew
R4,019 Discovery Miles 40 190 Ships in 12 - 17 working days

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

Hydrological Data Driven Modelling - A Case Study Approach (Paperback, Softcover reprint of the original 1st ed. 2015): Renji... Hydrological Data Driven Modelling - A Case Study Approach (Paperback, Softcover reprint of the original 1st ed. 2015)
Renji Remesan, Jimson Mathew
R3,746 Discovery Miles 37 460 Ships in 10 - 15 working days

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

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