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

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

Abstract

The study results identified impervious area and area of the watershed to be the most and the least important parameter with respect to the objective and based on different criteria like climatic, financial and urbanization impacts. The accuracy of ANN model was found to be more than 99 %. The results from the sensitivity analysis depict that the sensitivity and weight of importance of the parameter are totally coherent. The Nile River Basin and Yenisei River Basin were found to be the most and the least vulnerable watersheds, but in face of climate change River Mississippi in the USA becomes the most vulnerable in B2 scenario and the least vulnerable in A2 scenario.

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Authors and Affiliations

  1. Management, Bengal Institute of Technology and, Kolkata, India

    Uttam Roy

  2. National Institute of Technology, Agartala, India

    Mrinmoy Majumder

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  1. Uttam Roy
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  2. Mrinmoy Majumder
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Correspondence to Uttam Roy .

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Roy, U., Majumder, M. (2016). Results and Discussions. In: Vulnerability of Watersheds to Climate Change Assessed by Neural Network and Analytical Hierarchy Process. SpringerBriefs in Water Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-287-344-6_5

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