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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4152140, 8 pages
https://doi.org/10.1155/2017/4152140
Research Article

Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

1Department of Electrical Engineering, National Institute of Technology Manipur, Manipur, India
2Department of Computer Science & Engineering, National Institute of Technology Manipur, Manipur, India
3Department of Electrical Engineering, NERIST, Nirjuli, India

Correspondence should be addressed to Benjamin A. Shimray; moc.liamg@yarmihsnimajneb

Received 25 June 2016; Revised 7 January 2017; Accepted 9 February 2017; Published 26 February 2017

Academic Editor: Elio Masciari

Copyright © 2017 Benjamin A. Shimray et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India.