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

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 locat...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2017-01, Vol.2017 (2017), p.1-8
Main Authors: Mehta, R. K., Khelchandra, Thongam, Singh, Kh. Manglem, Shimray, Benjamin A.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Back propagation
Case studies
Computer engineering
Decision making
Electric power generation
Electric power plants
Electrical engineering
Environment
Fuzzy logic
Genetic algorithms
Geographic information systems
Humans
Hydroelectric power plants
India
Industrial locations
Laws
Linguistics
Location
Methods
Models, Theoretical
Neural networks
Neural Networks (Computer)
Nitrogen dioxide
Power Plants
Ranking
Site selection
Water quality
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2017/4152140