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Hydrological Data Driven Modelling: A Case Study Approach

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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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Detail Information

Series Title

-

Call Number

550 HYD

Publisher

: ., 2015

Collation

XV, 250

Language

English

ISBN/ISSN

978-3-319-09234-8

Classification

550

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Geology

Specific Detail Info

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Statement of Responsibility

Renji Remesan , Jimson Mathew editor

Other Information

Cataloger

Khusnun

Source

-

Validator

-

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