Hydrological Data Driven Modelling

A Case Study Approach

Nonfiction, Science & Nature, Science, Other Sciences, Meteorology, Earth Sciences
Cover of the book Hydrological Data Driven Modelling by Renji Remesan, Jimson Mathew, Springer International Publishing
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Author: Renji Remesan, Jimson Mathew ISBN: 9783319092355
Publisher: Springer International Publishing Publication: November 3, 2014
Imprint: Springer Language: English
Author: Renji Remesan, Jimson Mathew
ISBN: 9783319092355
Publisher: Springer International Publishing
Publication: November 3, 2014
Imprint: Springer
Language: English

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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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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