Hydrological Data Driven Modelling

URL: http://www.springer.com/us/book/9783319092348?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBook

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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Additional Information

Field Value
Last updated June 16, 2016
Created June 16, 2016
Format unknown
License No License Provided
created over 2 years ago
id 25f92e70-eda0-4a4b-b145-06d7faeae404
openAccess false
package id b0b32b0f-aa1a-43fe-9de8-0d6fcc872fb8
position 2
revision id f0a5a351-7af0-466a-aa97-47988e9757d6
state active
tags Modelling,neural networks,svm,AI
topic Water supply and distribution
type Book
year 2,015