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

Nonfiction, Science & Nature, Science, Physics, Energy, Earth Sciences, Technology
Cover of the book Vulnerability of Watersheds to Climate Change Assessed by Neural Network and Analytical Hierarchy Process by Uttam Roy, Mrinmoy Majumder, Springer Singapore
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Author: Uttam Roy, Mrinmoy Majumder ISBN: 9789812873446
Publisher: Springer Singapore Publication: December 28, 2015
Imprint: Springer Language: English
Author: Uttam Roy, Mrinmoy Majumder
ISBN: 9789812873446
Publisher: Springer Singapore
Publication: December 28, 2015
Imprint: Springer
Language: English

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.

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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.

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