Ranking of sites for power plant installation using soft computing techniques – A thought beyond EIA

Research on decision making in an imprecise environment has gained and will continue to gain overriding importance in recent years. In this context, decision making on the location of hazardous plant installations is an issue of relevance. The authors believe that there is a need of a standard model to overcome the discrepancies in Environmental Impact Assessment (EIA) for rationalization of site selection. An inappropriate choice of a site for an upcoming hazardous industrial plant may lead to further degradation of existing environment thus resulting in hazardous effects on mankind as well as all other living organisms. It could be argued that there exists quantifiable (measurable parameters) and epistemic (non measurable parameters) uncertainties in the scoping phase and the overall EIA process. The uncertainties could be modeled using soft computing techniques in ranking the sites for upcoming hazardous industry and justify the results in linguistic terms. The case study in the paper relates to the application of Back propagation Artificial Neural Network (BP-ANN), Learning Vector Quantization (LVQ-ANN), Ant Colony optimization with Fuzzy Soft Sets (FSS) and Fuzzy Indexing (FI), in ranking the existing/upcoming power plant locations in India. Comparative evaluation of these methods is also an integral part of the paper.

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