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

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.

Mr. Uttam Roy is a Senior Faculty of Mechanical Engineering in Bengal Institute of Technology and Management, India and a Part time Research Scholar in School of Hydro-Informatics Engineering, National Institute of Technology Agartala, India. He is a Master of Mechanical Engineering and has published seven papers in international journals.
Dr. Mrinmoy Majumder is presently working as Assistant Professor in School of Hydro-Informatics Engineering of National Institute of Technology, Agartala, Tripura, India from the year of 2010. He has completed his PhD from Jadhavpur University, Kolkata, West Bengal, India in the year of 2010. He has published more than 25 research papers in national and international journals and has published five books from reputed international publishers.

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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.