Hydropower is found to be one of the most reliable and inexpensive options for renewable energy which was now widely been adopted by many countries to substitute fossil fuel sources. This Brief highlights the impact of climate change on hydropower plants, especially on the turbine design, as turbines are responsible for optimal conversion and regular energy production. The vulnerability of turbines is analyzed with the help of Artificial Neural Networks, followed by Multi Criteria Decision Making methods for development of intelligent indices to represent the level of vulnerability of turbines due to the change in climate.

Autorentext
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 Jadavpur 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.

Inhalt
Introduction.- Small Scale Hydro Power Plant.- Objective of the Present Investigation.- Brief Methodology.- Climate Change and its Impacts.- Climate change : cause and effects.- Impacts on Hydrological Cycle.- Impacts on Watersheds.- IPCC Scenarios.- Climate Models.- Hydropower Plant.- Working Principle.- Types of Hydro-Power Plant.- Case Studies.- Turbine Vulnerabilities.- Types of Vulnerabilities.- Impact on Efficiency.- Factors for detection of Vulnerability in turbines.- Similar Indices representing Vulnerability.-Methodology.- Application of Analytical Hierarchy Process.- Development of Vulnerability Index for Turbines.- Development of the Artificial Neural Network Model.- Ranking of Selected Watersheds.- Results and discussions.- Results.- Comparison with other similar studies.- Scientific Implications.- Assumptions/Limitations.- Conclusion.- Summary.- Limitations.- Futurescope.