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An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

Author

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  • Seyed Morteza Hatefi

    (Department of Civil Engineering, Faculty of Engineering, Shahrekord University, Rahbar Boulevard, P.O. Box 115 Shahrekord, Iran)

  • Mohammad Ehsan Basiri

    (Department of Computer Engineering, Faculty of Engineering, Shahrekord University, Rahbar Boulevard, P.O. Box 115 Shahrekord, Iran)

  • Jolanta Tamošaitienė

    (Institute of Sustainable Construction, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

Abstract

One of the goals of sustainable development is to achieve economic and social growth according to environmental criteria. Nowadays, impact assessment is an efficient decision making method in planning and management with environmental perspectives. Environmental risk assessment is a tool to reduce the impacts and consequences of various activities on the environment in order to achieve sustainable development. One of the commonly used environmental risk assessment methods is the probability–impact matrix method, which is known as a quantitative method for risk assessment of projects. In this method, numerical estimates of probability and impact of risk occurrence are very difficult, and these factors are associated with uncertainty. When uncertainty exists, data integration is of great importance, for which the fuzzy inference system and evidence theory are known as effective methods. Unavailability of experts’ opinion and the exponential growth of the number of required fuzzy rules associated with the risk factors are two drawbacks of fuzzy inference. Dempster–Shafer’s theory of evidence is one of the popular theories used in intelligent systems for modeling and reasoning under uncertainty and inaccuracy. In this paper, an evidential model for project environmental risk assessment is proposed based on the Dempster–Shafer theory, which is capable of taking into account the uncertainties. The proposed model is used to assess the environmental risks of Maroon oil pipelines in Isfahan. In addition, the proposed model is used in the case of tunneling risk assessment taken from the subject literature. To evaluate the validity of the proposed evidential model, the results are compared in two case studies, with the results of the conventional risk assessment method and the fuzzy inference system method. The comparative results show that the proposed model has a high potential for project risk assessment under an uncertain environment.

Suggested Citation

  • Seyed Morteza Hatefi & Mohammad Ehsan Basiri & Jolanta Tamošaitienė, 2019. "An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6329-:d:285828
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    References listed on IDEAS

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    1. Foroogh Ghasemi & Mohammad Hossein Mahmoudi Sari & Vahidreza Yousefi & Reza Falsafi & Jolanta Tamošaitienė, 2018. "Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    2. J. H. M. Tah & V. Carr, 2000. "A proposal for construction project risk assessment using fuzzy logic," Construction Management and Economics, Taylor & Francis Journals, vol. 18(4), pages 491-500.
    3. Sukran Seker & Edmundas Kazimieras Zavadskas, 2017. "Application of Fuzzy DEMATEL Method for Analyzing Occupational Risks on Construction Sites," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    4. Antonio Nesticò & Shuquan He & Gianluigi De Mare & Renato Benintendi & Gabriella Maselli, 2018. "The ALARP Principle in the Cost-Benefit Analysis for the Acceptability of Investment Risk," Sustainability, MDPI, vol. 10(12), pages 1-22, December.
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    Cited by:

    1. Jolanta Tamošaitienė & Vahidreza Yousefi & Hamed Tabasi, 2021. "Project Portfolio Construction Using Extreme Value Theory," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    2. Yutong Chen & Yongchuan Tang, 2021. "An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
    3. Saad Muslet Albogami & Mohd Khairol Anuar Bin Mohd Ariffin & Eris Elianddy Bin Supeni & Kamarul Arifin Ahmad, 2021. "A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    4. Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    5. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2021. "The Management Efficiency of the Sustainable Development Policy under Thailand s Energy Law: Enriching the SEM-based on the ARIMAXi model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 472-482.
    6. Abdar, Moloud & Basiri, Mohammad Ehsan & Yin, Junjun & Habibnezhad, Mahmoud & Chi, Guangqing & Nemati, Shahla & Asadi, Somayeh, 2020. "Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    7. Augustinas Maceika & Andrej Bugajev & Olga Regina Šostak & Tatjana Vilutienė, 2021. "Decision Tree and AHP Methods Application for Projects Assessment: A Case Study," Sustainability, MDPI, vol. 13(10), pages 1-33, May.

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