Research on decision-making of emergency plan for waterlogging disaster in subway station project based on linguistic intuitionistic fuzzy set and TOPSIS

Han Wu; Junwu Wang; Sen Liu; Tingyou Yang; Han Wu; Junwu Wang; Sen Liu; Tingyou Yang

doi:10.3934/mbe.2020263

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 4825-4851. doi: 10.3934/mbe.2020263

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Research on decision-making of emergency plan for waterlogging disaster in subway station project based on linguistic intuitionistic fuzzy set and TOPSIS

1.
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
2.
CCTEB Infrastructure Construction Investment Co., Ltd, Wuhan 430070, China

Received: 27 May 2020 Accepted: 02 July 2020 Published: 10 July 2020

Targeted at emergency plans for rainstorm and waterlogging disasters in subway station projects, this work proposes a group decision-making method that uses linguistic intuitionistic fuzzy sets, structural entropy weights, and TOPSIS. An evaluation index system of emergency plans was constructed based on four aspects, namely a scientific basis, completeness, operability, and flexibility. A linguistic interval intuitionistic fuzzy set approach was then used to qualitatively present the decision-makers' understanding of, attitudes about, and preferences for emergency plans. The uncertainty was comprehensively and intuitively represented by the dimensions of the degrees of membership and non-membership. The structural entropy weight method was applied and improved to fully reflect the influences of experts with different characteristics on the index weights. Finally, the TOPSIS method, with a background context of linguistic interval intuitionistic fuzzy sets, was applied. The calculation results of benchmark and verification case highlight the rationality and scientificity of the method proposed in this paper. The emergency decisions regarding waterlogging in 2018 for the Huilong Road West Station Project of Chengdu Metro Line 11 in China were selected as a case study. The case study demonstrates that operability is the most critical of the four primary indicators, and that flexible response to changes in the emergency response level is the most important of the secondary indicators. The uncertainty analysis of data revealed that with the increase of uncertainty, the difference between each scheme and the ideal solution decreased. Compared with the classical TOPSIS method, the new model proposed in this paper is robust and effective, and can be used for similar projects in the future.
- subway station project,
- waterlogging disasters,
- emergency decisions,
- intuitionistic fuzzy sets of language,
- structural entropy weight method,
- TOPSIS
Citation: Han Wu, Junwu Wang, Sen Liu, Tingyou Yang. Research on decision-making of emergency plan for waterlogging disaster in subway station project based on linguistic intuitionistic fuzzy set and TOPSIS[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4825-4851. doi: 10.3934/mbe.2020263

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Abstract

Targeted at emergency plans for rainstorm and waterlogging disasters in subway station projects, this work proposes a group decision-making method that uses linguistic intuitionistic fuzzy sets, structural entropy weights, and TOPSIS. An evaluation index system of emergency plans was constructed based on four aspects, namely a scientific basis, completeness, operability, and flexibility. A linguistic interval intuitionistic fuzzy set approach was then used to qualitatively present the decision-makers' understanding of, attitudes about, and preferences for emergency plans. The uncertainty was comprehensively and intuitively represented by the dimensions of the degrees of membership and non-membership. The structural entropy weight method was applied and improved to fully reflect the influences of experts with different characteristics on the index weights. Finally, the TOPSIS method, with a background context of linguistic interval intuitionistic fuzzy sets, was applied. The calculation results of benchmark and verification case highlight the rationality and scientificity of the method proposed in this paper. The emergency decisions regarding waterlogging in 2018 for the Huilong Road West Station Project of Chengdu Metro Line 11 in China were selected as a case study. The case study demonstrates that operability is the most critical of the four primary indicators, and that flexible response to changes in the emergency response level is the most important of the secondary indicators. The uncertainty analysis of data revealed that with the increase of uncertainty, the difference between each scheme and the ideal solution decreased. Compared with the classical TOPSIS method, the new model proposed in this paper is robust and effective, and can be used for similar projects in the future.

References

[1]	J. Xia, Y. Zhang, L. Xiong, S. He, L. Wang, Z. Yu, Opportunities and challenges of the Sponge City construction related to urban water issues in China, Sci. China Earth Sci., 60 (2017), 652-658.
[2]	X. Wu, D. Yu, Z. Chen, R. Wilby, An evaluation of the impacts of land surface modification, storm sewer development, and rainfall variation on waterlogging risk in Shanghai, Nat. Hazards, 63 (2012), 305-323.
[3]	H. Yu, C. Liang, P. Li, K. Niu, F. Du, J. Shao, et al., Evaluation of waterlogging risk in an urban subway station, Adv. Civ. Eng., 2019 (2019), 5393171.
[4]	B. Konig, K. Diehl, K. Tscherning, K. Helming, A framework for structuring interdisciplinary research management, Res. Policy, 42 (2013), 261-272.
[5]	G. Chen, X. Zhang, Fuzzy-based methodology for performance assessment of emergency planning and its application, J. Loss Prev. Process Ind., 22 (2009), 125-132.
[6]	P. Ren, Z. Xu, J. Gu, Assessments of the effectiveness of an earthquake emergency plan implementation with Hesitant Analytic Hierarchy Process, Int. J. Inf. Technol. Decis. Making, 15 (2016), 1367-1389.
[7]	A. J. Maule, G. R. J. Hockey, L. Bdzola, Effects of time-pressure on decision-making under uncertainty: Changes in affective state and information processing strategy, Acta Psychol., 104 (2000), 283-301.
[8]	A. A. Baksh, F. Khan, V. Gadag, R. Ferdous, Network based approach for predictive accident modelling, Saf. Sci., 80 (2015), 274-287.
[9]	D. Gkatzia, O. Lemon, V. Rieser, Data-to-Text generation improves decision-making under uncertainty, IEEE Comput. Intell. Mag., 12 (2017), 10-17.
[10]	Y. Song, X. Wang, L. Lei, A. Xue, Combination of interval-valued belief structures based on intuitionistic fuzzy set, Knowl. Based Syst., 67 (2014), 61-70.
[11]	J. Wang, T. Sun, X. Chen, Multi-criteria fuzzy decision-making method based on prospect theory with incomplete information, Control. Decis, 24 (2009), 1198-1202.
[12]	J. Ye, Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment, Eur. J. Oper. Res., 205 (2010), 202-204.
[13]	R. M. Fang, R. Y. Shang, Y. D. Wang, X. Guo, Identification of vulnerable lines in power grids with wind power integration based on a weighted entropy analysis method, Int. J. Hydrogen Energy, 42 (2017), 20269-20276.
[14]	X. Liang, W. Liang, L. Zhang, X. Guo, Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement, J. Cleaner Prod., 227 (2019), 218-228.
[15]	F. Liu, S. Zhao, M. Weng, Y. Liu, Fire risk assessment for large-scale commercial buildings based on structure entropy weight method, Saf. Sci., 94 (2017), 26-40.
[16]	M. M. De Brito, M. Evers, Multi-criteria decision-making for flood risk management: A survey of the current state of the art, Nat. Hazards Earth Syst. Sci., 16 (2016), 1019-1033.
[17]	H. Shih, H. Shyur, E. S. Lee, An extension of TOPSIS for group decision making, Math. Comput. Modell., 45 (2007), 801-813.
[18]	C. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets Syst., 114 (2000), 1-9.
[19]	Y. Wang, H. Zhao, F. M. Duan, Y. Wang, Initial provincial allocation and equity evaluation of China's carbon emission rights-based on the improved TOPSIS method, Sustainability, 10 (2018), 1-27.
[20]	Y. Wang, Y. Liang, H. Sun, A regret theory-based decision-making method for urban rail transit in emergency response of rainstorm disaster, J. Adv. Transpt., 2020 (2020), 3235429.
[21]	P. Chen, J. Zhang, Y. Sun, X. Liu, Wargame simulation theory and evaluation method for emergency evacuation of residents from urban waterlogging disaster area, Int. J. Environ. Res. Public Health, 13 (2016), 1260.
[22]	L. Cheong, C. Kinkeldey, I. Burfurd, S. Bleisch, M. Duckham, et al., Evaluating the impact of visualization of risk upon emergency route-planning, Int. J. Geogr. Inf. Sci., 34 (2020), 1022-1050.
[23]	D. Kannan, R. Khodaverdi, L. Olfat, A. Jafarian, A. Diabat, Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain, J. Cleaner Prod., 47 (2013), 355-367.
[24]	X. Xu, B. Pan, Y. Yang, Large-group risk dynamic emergency decision method based on the dual influence of preference transfer and risk preference, Soft Comput., 22 (2018), 7479-7490.
[25]	J. Chang, Z. Chen, S. Xiong, J. Zhang, K. Chin, Intuitionistic fuzzy multiple criteria group decision making: A consolidated model with application to emergency plan selection, IEEE Access, 7 (2019), 41958-41980.
[26]	R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, B. Veitch, Handling and updating uncertain information in bow-tie analysis, J. Loss Prev. Process Ind., 25 (2012), 8-19.
[27]	C. Grose, Sources of uncertainty in Swedish emergency response planning, J. Risk Res., 22 (2019), 758-772.
[28]	J. Zhang, G. G. Hegde, J. Shang, X. Qi, Evaluating emergency response solutions for sustainable community development by using fuzzy multi-criteria group decision making approaches: IVDHF-TOPSIS and IVDHF-VIKOR, Sustainability, 8 (2016), 291.
[29]	J. Gao, Z. Xu, H. Liao, A dynamic reference point method for emergency response under hesitant probabilistic fuzzy environment, Int. J. Fuzzy Syst., 19 (2017), 1261-1278.
[30]	B. Z. Sun, W. M. Ma, An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set, Kybernetes, 45 (2016), 461-473.
[31]	F. Y. Meng, J. Tang, H. Fujita, Linguistic intuitionistic fuzzy preference relations and their application to multi-criteria decision making, Inf. Fusion, 46 (2019), 77-90.
[32]	Y. Ou, L. Yi, B. Zou, Z. Pei, The linguistic intuitionistic fuzzy set TOPSIS method for linguistic multi-criteria decision makings, Int. J. Comput. Intell. Syst., 11 (2018), 120-132.
[33]	E. Guo, J. Zhang, Y. Wang, H. Si, F. Zhang, Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China, Nat. Hazards, 83 (2016), 1747-1761.
[34]	Z. E. Yin, J. Yin, S. Y. Xu, J. Wen, Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging, J. Geogr. Sci., 21 (2011) 274-284.
[35]	H. Garg, K. Kumar, Linguistic interval-valued atanassov intuitionistic fuzzy sets and their applications to group decision making problems, IEEE Trans. Fuzzy Syst., 27 (2019), 2302-2311.
[36]	J. Ye, Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment, Expert Syst. Appl., 36 (2009), 6899-6902.
[37]	D. Yu, Y. Wu, T. Lu, Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making, Knowl. Based Syst., 30 (2012), 57-66.
[38]	Y. Ju, S. Yang, A new method for multiple attribute group decision-making with intuitionistic trapezoid fuzzy linguistic information, Soft Comput., 19 (2015), 2211-2224.
[39]	X. Qi, C. Liang, J. Zhang, Some generalized dependent aggregation operators with interval-valued intuitionistic fuzzy information and their application to exploitation investment evaluation, J. Appl. Math., 2013 (2013), 1-24.
[40]	Z. Chen, K. Chin, Y. Li, Y. Yang, Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making, Inf. Sci., 357 (2016), 61-87.
[41]	P. Y. Chen, A Novel Coordinated TOPSIS Based on Coefficient of Variation, Mathematics, 7 (2019), 614.
[42]	H. S. Ma, J. X. Gao, Q. Zhao, Real estate residential location research based on balance expectations, J. Eng. Manage., 30 (2016) 143-147.
[43]	X. Liu, Y. Ju, S. Yang, Hesitant intuitionistic fuzzy linguistic aggregation operators and their applications to multiple attribute decision making, J. Intell. Fuzzy Syst., 27 (2014), 1187-1201.
[44]	R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, B. Veitch, Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach, Process Saf. Environ. Prot., 91 (2013), 1-18.
[45]	H. Bustince, C. Marco-Detchart, J. Fernandez, C. Wagner, J. M. Garibaldi, Z. Takac, Similarity between interval-valued fuzzy sets taking into account the width of the intervals and admissible orders, Fuzzy Sets Syst., 390 (2020), 23-47.

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