A modified D numbers methodology for environmental impact assessment
Abstract
Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to represent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations represented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.
First published online: 09 May 2017
Keyword : D numbers theory, EIA, decision making, uncertainty, incompleteness, fuzziness
How to Cite
Wang, N., & Wei, D. (2018). A modified D numbers methodology for environmental impact assessment. Technological and Economic Development of Economy, 24(2), 653–669. https://doi.org/10.3846/20294913.2016.1216018
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Dempster, A. P. 1967. Upper and lower probabilities induced by a multivalued mapping, The Annals of Mathematical Statistics 38(2): 325–339. https://doi.org/10.1214/aoms/1177698950
Deng, X. Y.; Hu, Y.; Deng, Y. 2014a. Bridge condition assessment using D numbers, The Scientific World Journal 2014(5): 1–10.
Deng, X. Y.; Hu, Y.; Deng, Y.; Mahadevan, S. 2014b. Environmental impact assessment based on D numbers, Expert Systems with Applications 41(2): 635–643. https://doi.org/10.1016/j.eswa.2013.07.088
Deng, X. Y.; Hu, Y.; Deng, Y.; Mahadevan, S. 2014c. Supplier selection using AHP methodology extended by D numbers, Expert Systems with Applications 41(1): 156–167. https://doi.org/10.1016/j.eswa.2013.07.018
Deng, X. Y.; Lu, X.; Chan, F. T. S.; Sadiq, R.; Mahadevan, S.; Deng, Y. 2014d. D-CFPR: D numbers extended consistent fuzzy preference relations, Knowledge-Based Systems 73: 61–68. https://doi.org/10.1016/j.knosys.2014.09.007
Deng, Y. 2012. D numbers: theory and applications, Journal of Information and Computational Science 9(9): 2421–2428.
Deng, Y. 2015a. Generalized evidence theory, Applied Intelligence 43(3): 530–543. https://doi.org/10.1007/s10489-015-0661-2
Deng, Y. 2015b. A threat assessment model under uncertain environment, Mathematical Problems in Engineering 2015, Article ID 878024. https://doi.org/10.1155/2015/878024
Deng, Y.; Jiang, W.; Sadiq, R. 2011a. Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method, Expert Systems with Applications 38(1): 571–578. https://doi.org/10.1016/j.eswa.2010.07.004
Deng, Y.; Liu, Y.; Zhou, D. Y. 2015a. An improved genetic algorithm with initial population strategy for symmetric TSP, Mathematical Problems in Engineering 2015, Article ID 212794. https://doi.org/10.1155/2015/212794
Deng, Y.; Mahadevan, S.; Zhou, D. Y. 2015b. Vulnerability assessment of physical protection systems: a bio-inspired approach, International Journal of Unconventional Computing 11(3–4): 227–243.
Deng, Y.; Sadiq, R.; Jiang, W.; Tesfamariam, S. 2011b. Risk analysis in a linguistic environment: a fuzzy evidential reasoning-based approach, Expert Systems with Applications 38(12): 15438–15446. https://doi.org/10.1016/j.eswa.2011.06.018
Dreyer, L.; Hauschild, M.; Schierbeck, J. 2006. A framework for social life cycle impact assessment, The International Journal of Life Cycle Assessment 11(2): 88–97. https://doi.org/10.1065/lca2005.08.223
Fan, G. C.; Zhong, D. H.; Yan, F. G.; Yue, P. 2016. A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers, Expert Systems with Applications 44: 289–303. https://doi.org/10.1016/j.eswa.2015.09.006
Fu, C.; Chin, K. S. 2014. Robust evidential reasoning approach with unknown attribute weights, Knowledge-Based Systems 59: 9–20. https://doi.org/10.1016/j.knosys.2014.01.024
Fu, C.; Yang, S. L. 2012. An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements, European Journal of Operational Research 223(1): 167–176. https://doi.org/10.1016/j.ejor.2012.05.048
Hashemkhani Zolfani, S.; Aghdaie, M. H.; Derakhti, A.; Zavadskas, E. K.; Varzandeh, M. H. M. 2013. Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating, Expert Systems with Applications 40(17): 7111–7121. https://doi.org/10.1016/j.eswa.2013.06.040
Hu, Q. Z.; Lu, H. P. 2009. Rough set comprehensive evaluation model for environmental security effect of highway construction projects, in Energy and Environment Technology 2009 (ICEET’09), 16–18 October 2009, Guilin, China, 301–304.
Jiang, W.; Luo, Y.; Qin, X. Y.; Zhan, J. 2015a. An improved method to rank generalized fuzzy numbers with different left heights and right heights, Journal of Intelligent and Fuzzy Systems 28(5): 2343–2355. https://doi.org/10.3233/IFS-151639
Jiang, W.; Yang, Y.; Luo, Y.; Qin, X. Y. 2015b. Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers, International Journal of Computers Communications and Control 10(3): 333–347. https://doi.org/10.15837/ijccc.2015.3.1656
Jordan, Y. C.; Ghulam, A.; Chu, M. L. 2014. Assessing the impacts of future urban development patterns and climate changes on total suspended sediment loading in surface waters using geoinformatics, Journal of Environmental Informatics 24(2): 65–79. https://doi.org/10.3808/jei.201400283
Ju, H. C.; Yoo, S. H. 2014. Using the fuzzy set theory to developing an environmental impact assessment index for a thermal power plant, Quality and Quantity 48(2): 673–680. https://doi.org/10.1007/s11135-012-9794-0
Kaplinski, O.; Peldschus, F.; Tupenaite, L. 2014. Development of MCDM methods-in honour of Professor Edmundas Kazimieras Zavadskas on the occasion of his 70th birthday, International Journal of Computers Communications and Control 9(3): 305–312. https://doi.org/10.15837/ijccc.2014.3.1084
Khan, M.; Fitzcharles, K. 1998. Environmental management field handbook for rural road improvement projects. CARE International Bangladesh, Dhaka.
Kiliç, E.; Puig, R.; Baquero, G.; Font, J.; Çolak, S.; Gürler, D. 2011. Environmental optimization of chromium recovery from tannery sludge using a life cycle assessment approach, Journal of Hazardous Materials 192(1): 393–401.
Lenzen, M.; Murray, S. A.; Korte, B.; Dey, C. J. 2003. Environmental impact assessment including indirect effects – a case study using input-output analysis, Environmental Impact Assessment Review 23(3): 263–282. https://doi.org/10.1016/S0195-9255(02)00104-X
Liu, B. D. 2014. Uncertainty theory. Springer.
Liu, H. C.; You, J. X.; Fan, X. J.; Lin, Q. L. 2014a. Failure mode and effects analysis using D numbers and grey relational projection method, Expert Systems with Applications 41(10): 4670–4679. https://doi.org/10.1016/j.eswa.2014.01.031
Liu, Z. G.; Pan, Q.; Dezert, J. 2014b. Classification of uncertain and imprecise data based on evidence theory, Neurocomputing 133: 459–470. https://doi.org/10.1016/j.neucom.2013.12.009
Ma, Z. Z.; Wang, Z. J.; Xia, T.; Gippel, C. J.; Speed, R. 2014. Hydrograph-based hydrologic alteration assessment and its application to the yellow river, Journal of Environmental Informatics 23(1): 1–13. https://doi.org/10.3808/jei.201400252
Mardani, A.; Jusoh, A.; Zavadskas, E. K. 2015. Fuzzy multiple criteria decision-making techniques and applications-two decades review from 1994 to 2014, Expert Systems with Applications 42(8): 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
Miao, D. Y.; Huang, W. W.; Li, Y. P.; Yang, Z. F. 2014. Planning water resources systems under uncertainty using an interval-fuzzy de novo programming method, Journal of Environmental Informatics 24(1): 11–23. https://doi.org/10.3808/jei.201400277
Morón, A. B.; Calvo-Flores, M. D.; Ramos, J. M.; Almohano, M. P. 2009. AIEIA: Software for fuzzy environmental impact assessment, Expert Systems with Applications 36(5): 9135–9149. https://doi.org/10.1016/j.eswa.2008.12.055
Ni, J. R.; Wu, A.; Li, T. H.; Yue, Y.; Borthwick, A. G. L. 2014. Efficient soil loss assessment for large basins using smart coded polygons, Journal of Environmental Informatics 23(2): 47–57. https://doi.org/10.3808/jei.201400264
Pastakia, C. M.; Jensen, A. 1998. The rapid impact assessment matrix (RIAM) for EIA, Environmental Impact Assessment Review 18(5): 461–482. https://doi.org/10.1016/S0195-9255(98)00018-3
Pawlak, Z.; Skowron, A. 2007. Rudiments of rough sets, Information Sciences 177(1): 3–27. https://doi.org/10.1016/j.ins.2006.06.003
Pun, K. F.; Hui, I. K.; Lewis, W. G.; Lau, H. C. 2003. A multiple-criteria environmental impact assessment for the plastic injection molding process: a methodology, Journal of Cleaner Production 11(1): 41–49. https://doi.org/10.1016/S0959-6526(02)00019-7
Rabbani, A.; Zamani, M.; Yazdani-Chamzini, A.; Zavadskas, E. K. 2014. Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies, Expert Systems with Applications 41(16): 7316–7327. https://doi.org/10.1016/j.eswa.2014.05.023
Riga, M.; Stocker, M.; Rönkkö, M.; Karatzas, K.; Kolehmainen, M. 2015. Atmospheric environment and quality of life information extraction from twitter with the use of self-organizing maps, Journal of Environmental Informatics 26(1): 27–40.
Rikhtegar, N.; Mansouri, N.; Oroumieh, A. A.; Yazdani-Ccamzini, A.; Zavadskas, E. K.; Kildienė, S.; 2014. Environmental impact assessment based on group decision-making methods in mining projects, Economic Research-Ekonomska Istraživanja 27(1): 378–392. https://doi.org/10.1080/1331677X.2014.966971
Sadiq, R.; Kleiner, Y.; Rajani, B. 2006. Estimating risk of contaminant intrusion in water distribution networks using Dempster-Shafer theory of evidence, Civil Engineering and Environmental Systems 23(3): 129–141. https://doi.org/10.1080/10286600600789276
Shafer, G. 1976. A mathematical theory of evidence. Princeton University Press.
Su, X. Y.; Mahadevan, S.; Xu, P. D.; Deng, Y. 2015. Dependence assessment in human reliability analysis using evidence theory and AHP, Risk Analysis 35(7): 1296–1316. https://doi.org/10.1111/risa.12347
Sueyoshi, T.; Goto, M. 2012. Data envelopment analysis for environmental assessment: comparison between public and private ownership in petroleum industry, European Journal of Operational Research 216(3): 668–678. https://doi.org/10.1016/j.ejor.2011.07.046
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