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Hydrological Data Driven Modelling A Case Study Approach

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...

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Bibliographic Details
Main Authors: Remesan, Renji, Mathew, Jimson (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2015, 2015
Edition:1st ed. 2015
Series:Earth Systems Data and Models
Subjects:
Geology
Engineering Geology
Water
Geoengineering
Hydrology
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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505 0 |a Introduction -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Model Data Selection and Data Pre-processing Approaches -- Machine Learning and Artificial Intelligence Based Approaches -- Data based Solar Radiation Modelling -- Data based Rainfall-Runoff Modelling -- Data based Evapotranspiration Modelling -- Application of Statistical Blockade in Hydrology 
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520 |a 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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