Hydrological Data Driven Modelling

A Case Study Approach

  • Renji Remesan
  • Jimson Mathew

Part of the Earth Systems Data and Models book series (ESDM, volume 1)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Renji Remesan, Jimson Mathew
    Pages 1-17
  3. Renji Remesan, Jimson Mathew
    Pages 19-39
  4. Renji Remesan, Jimson Mathew
    Pages 41-70
  5. Renji Remesan, Jimson Mathew
    Pages 71-110
  6. Renji Remesan, Jimson Mathew
    Pages 111-150
  7. Renji Remesan, Jimson Mathew
    Pages 151-182
  8. Renji Remesan, Jimson Mathew
    Pages 183-230
  9. Renji Remesan, Jimson Mathew
    Pages 231-247
  10. Back Matter
    Pages 249-250

About this book


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.


Applied hydrology Artificial intelligence in hydrology Evapotranspiration modelling Hydrologic modelling Rainfall-Runoff modelling Solar radiation Support vector Time series modelling

Authors and affiliations

  • Renji Remesan
    • 1
  • Jimson Mathew
    • 2
  1. 1.Cranfield UniversityCranfield Water Science InstituteCranfieldUnited Kingdom
  2. 2.Department of Computer ScienceUniversity of BristolBristolUnited Kingdom

Bibliographic information

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