Vulnerability of Watersheds to Climate Change Assessed by Neural Network and Analytical Hierarchy Process

  • Uttam Roy
  • Mrinmoy Majumder

Part of the SpringerBriefs in Water Science and Technology book series (BRIEFSWATER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Uttam Roy, Mrinmoy Majumder
    Pages 1-15
  3. Uttam Roy, Mrinmoy Majumder
    Pages 17-23
  4. Uttam Roy, Mrinmoy Majumder
    Pages 25-32
  5. Uttam Roy, Mrinmoy Majumder
    Pages 33-46
  6. Uttam Roy, Mrinmoy Majumder
    Pages 47-86
  7. Uttam Roy, Mrinmoy Majumder
    Pages 87-89

About this book


The increase in GHG gases in the atmosphere due to expansions in industrial and vehicular concentration is attributed to warming of the climate world wide. The resultant change in climatic pattern can induce abnormalities in the hydrological cycle. As a result, the regular functionality of river watersheds will also be affected. This Brief highlights a new methodology to rank the watersheds in terms of its vulnerability to change in climate. This Brief introduces a Vulnerability Index which will be directly proportional to the climatic impacts of the watersheds. Analytical Hierarchy Process and Artificial Neural Networks are used in a cascading manner to develop the model for prediction of the vulnerability index.


Analytical Hierarchy Process Climate Change Hydrological Cycle Multi Criteria Decision Analysis (MCDA) Neural Networks Ranking if Watersheds Vulnerability Index for Turbines

Authors and affiliations

  • Uttam Roy
    • 1
  • Mrinmoy Majumder
    • 2
  1. 1.Bengal Institute of Technology and ManagementKolkataIndia
  2. 2.National Institute of TechnologyAgartalaIndia

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Singapore
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-981-287-343-9
  • Online ISBN 978-981-287-344-6
  • Series Print ISSN 2194-7244
  • Series Online ISSN 2194-7252
  • Buy this book on publisher's site
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