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E-raamat: Vulnerability of Watersheds to Climate Change Assessed by Neural Network and Analytical Hierarchy Process

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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.
1 Introduction
1(16)
1.1 Introduction
1(16)
1.1.1 Signs of Climate Change
2(1)
1.1.2 Climate Change and Its Impacts
2(8)
1.1.3 Watershed Vulnerabilities
10(1)
1.1.4 Indices Representing Watershed Vulnerability
10(2)
1.1.5 Objective of the Present Investigation
12(1)
1.1.6 Brief Methodology
13(1)
References
13(4)
2 Climate Change and Its Impacts
17(8)
2.1 Climate Change: Cause and Effects
17(2)
2.2 Impacts on Hydrological Cycle
19(1)
2.3 Impacts on Watersheds
19(1)
2.4 IPCC Scenarios
19(1)
2.5 Climate Models
19(6)
References
22(3)
3 Watershed Vulnerabilities
25(8)
3.1 Types of Watersheds
25(1)
3.2 Functions of Watersheds
25(3)
3.3 Factors of Vulnerability
28(1)
3.4 Indices Representing Vulnerability
28(5)
References
32(1)
4 Methodology
33(14)
4.1 Application of Analytical Hierarchy Process
34(3)
4.1.1 Selection of Criteria
34(2)
4.1.2 Selection of Alternatives
36(1)
4.1.3 Determination of Weights by AHP
36(1)
4.2 Development of Vulnerability Index
37(1)
4.3 Development of the Artificial Neural Network Model
37(1)
4.4 Ranking of Selected Watersheds
38(9)
4.4.1 Data Collection from Climatic Model
38(1)
4.4.2 Prediction from ANN Model
38(1)
4.4.3 Study Areas
39(7)
References
46(1)
5 Results and Discussions
47(40)
5.1 Results
47(37)
5.1.1 Result from AHP Application
47(2)
5.1.2 Application of ANN
49(7)
5.1.3 Performance Metrics of ANN Model
56(1)
5.1.4 Development of the Climate Model
57(27)
5.2 Comparison with Other Similar Studies
84(2)
5.3 Scientific Implications
86(1)
5.4 Assumptions/Limitations
86(1)
References
86(1)
6 Conclusion
87
6.1 Summary
87(2)
6.2 Limitations
89(1)
6.3 Future-Scope
89
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.