Efficacy of Selected Soft Computing Techniques in Ranking of Sites for Hazardous Industrial Installation

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 317)


Environmental impact assessment (EIA) is a process of identifying impact and risks that a proposed project (e.g., nuclear power plant installation) may have on the environment. The EIA methods require measurement of specific parameters and variables to estimate the values of impact indicators. However, many parameters and impact indicators in EIA cannot be measured precisely (e.g., lifestyle quality, social acceptance, etc.), and are sometimes very subjective in nature. In order to process this inaccurate and subjective information, we have used soft computing techniques to model the EIA process. In the present study we have implemented two well defined soft computing methods for EIA, namely, Fuzzy Indexing and Artificial Neural Networks (ANNs). The chapter presents a comparative evaluation of these methods with the existing BEES method.


Environmental impact assessment Fuzzy indexing Artificial neural networks Linear vector quantization 



We would like to express sincere gratitude to Dr. Mrs. S. S. Tikle, Environmental Science Department, University of Pune, India and other Experts recommended by her for their valuable opinion in assessing Environmental parameters and Reports.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PuneMaharashtraIndia
  2. 2.Evolutionary Computing and Image Processing GroupCentre for Development of Advanced ComputingPuneIndia
  3. 3.Berkeley Initiative Soft Computing, Special Interest GroupEnvironmental Management SystemBerkeleyUSA
  4. 4.College of EngineeringPuneIndia

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