Integrating case-based and fuzzy reasoning to qualitatively predict risk in an environmental impact assessment review

  • Authors:

  • Kevin Fong-Rey Liu;Chih-Wei Yu

  • Affiliations:

  • Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, Taipei 24301, Taiwan;Department of Environmental Engineering, Da-Yeh University, Changhua 51591, Taiwan

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2009

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Abstract

During the preparation of environmental impact statements (EIS) and environmental impact assessment reports (EIAR) for a development proposal, developers have three concerns. First is acquiring similar proposals for reference. Second is to forecast a possible review result for a compiled EIS or EIAR: approval, conditional approval, second-stage EIA, or disapproval. Risk management in accordance with the possible review result is the third issue. With the predicted possible review result, early preparation and revision of environmental management plans can ameliorate in advance highly risky nuisances; thereby the probability of passing review is relatively enhanced. In response to the first concern, Taiwan EPA provides developers an information system to access EISs and EIARs through the Internet. Except that, there is no related system to address these concerns in Taiwan. In this paper, the following suggestions of using artificial intelligence and management science are proposed to assist developers: case-based reasoning (CBR) for retrieval of similar cases, fuzzy reasoning (FR) for qualitative risk forecast, and importance-performance analysis (IPA) for risk management. Finally, a case study is used to demonstrate the use of the proposed system.