Skip to main content

Advertisement

Springer Nature Link
Log in

Supportive emergency decision-making model towards sustainable development with fuzzy expert system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The major key attributes of decision-making during emergency to de-escalate disaster, reduce fatality and prevent asset loss are time and the efficiency of the process. Decision-makers faced the challenge of accessing adequate and precise information during emergency cases due to the time limitation, inadequate data on and about the disasters and thus decision-making process becomes complex and complicated. A well-advanced and developed mathematical tool is required to respond adequately in the presence of these challenges. The current study investigates the effects of post-flood management plans in Iran through sustainable development features in the possible early time. A new hybrid emergency decision-making approach integrating the best–worst method (BWM), Z numbers and zero‐sum game is proposed to ensure much more effective responses in realistic cases. The importance weights of criteria are computed using the BWM, the payoff assessments of decision-makers are collected employing the Z numbers, and finally, the zero‐sum game method is utilized to rank the alternative of emergency solutions. The proposed hybrid approach assists the decision-makers to deal decisively with the ambiguity associated with the data for assessing and evaluating the emergency circumstances. To show the efficiency of the proposed approach, a real-life example of the Golestan flood of 2019 is presented. More so, a comparison analysis is performed to assess the practicability and feasibility of the proposed hybrid approach. The result indicates that the proposed methodology has considerable merits compared with the existing tools and can adequately deal with these shortages. In this case, the aircraft emergency delivery system of the relief supplies is obtained as the best solution to the problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(Source: Web of Science, keywords search: (Title: “emergency decision-making or emergency decision making” OR “emergency decision-making or emergency decision making”))

Fig. 2

(Source: Web of Science, keywords search: (source: Web of Science, keywords search: (Title: “emergency decision-making” OR “emergency decision making”))

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bankoff G, Frerks G, Hilhorst D (2004) Mapping Vulnerability: Disasters. Development and People, London

    Google Scholar 

  2. Zhou L, Wu X, Xu Z, Fujita H (2018) Emergency decision making for natural disasters: An overview. Int J Disaster Risk Reduct 27:567–576. https://doi.org/10.1016/j.ijdrr.2017.09.037

    Article  Google Scholar 

  3. The rising cost of catastrophes, Econ. (2012). https://www.economist.com/leaders/2012/01/14/the-rising-cost-of-catastrophes.

  4. Ebrahim M, Nastaran B, Timothy C (2020) Non-compensatory decision model for incorporating the sustainable development criteria in flood risk management plans. SN Appl Sci 2:1–11. https://doi.org/10.1007/s42452-019-1695-6

    Article  Google Scholar 

  5. Chitsaz N, Banihabib ME (2015) Comparison of different multi criteria decision-making models in prioritizing flood management alternatives. Water Resour Manage 29(8):2503–2525. https://doi.org/10.1007/s11269-015-0954-6

    Article  Google Scholar 

  6. Jiang GJ, Chen HX, Sun HH et al (2021) An improved multi-criteria emergency decision-making method in environmental disasters. Soft Comput. https://doi.org/10.1007/s00500-021-05826-x

  7. Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Chang 3:4–6. https://doi.org/10.1038/nclimate1911

    Article  Google Scholar 

  8. Ni J, Sun L, Li T, Huang Z, Borthwick AGL (2010) Assessment of fl ooding impacts in terms of sustainability in mainland China. J Environ Manage 91:1930–1942. https://doi.org/10.1016/j.jenvman.2010.02.010

    Article  Google Scholar 

  9. Rahmati O, Samadi M, Shahabi H, Azareh A, Ra E, Alilou H, Melesse AM, Pradhan B, Chapi K (2019) Geoscience Frontiers SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors. Geosci Front. https://doi.org/10.1016/j.gsf.2019.03.009

    Article  Google Scholar 

  10. Mostafazadeh R, Sadoddin A, Bahremand A, Sheikh VB, Garizi AZ (2017) Scenario analysis of flood control structures using a multi-criteria decision-making technique in Northeast. Nat Hazards. https://doi.org/10.1007/s11069-017-2851-1

    Article  Google Scholar 

  11. Vahedberdi S, Kornejady A, Ownegh M (2019) Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts. Springer, Netherlands

    Google Scholar 

  12. Ding XF, Liu HC (2019) A new approach for emergency decision-making based on zero-sum game with Pythagorean fuzzy uncertain linguistic variables. Int J Intell Syst 34:1667–1684. https://doi.org/10.1002/int.22113

    Article  Google Scholar 

  13. Li M, Cao P (2019) Computers & Industrial Engineering Extended TODIM method for multi-attribute risk decision making problems in emergency response. Comput Ind Eng 135:1286–1293. https://doi.org/10.1016/j.cie.2018.06.027

    Article  Google Scholar 

  14. Yazdi M, Kabir S, Walker M (2019) Uncertainty handling in fault tree based risk assessment: State of the art and future perspectives. Process Saf Environ Prot 131:89–104. https://doi.org/10.1016/j.psep.2019.09.003

    Article  Google Scholar 

  15. Zhang L, Wang Y, Zhao X (2018) Knowledge-Based Systems A new emergency decision support methodology based on multi-source knowledge in 2-tuple linguistic model. Knowledge-Based Syst 144:77–87. https://doi.org/10.1016/j.knosys.2017.12.026

    Article  Google Scholar 

  16. Daneshvar S, Yazdi M, Adesina KA (2020) Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system. Qual Reliab Eng Int. https://doi.org/10.1002/qre.2607

    Article  Google Scholar 

  17. Nawaz F, Asadabadi MR, Janjua NK, Hussain OK, Chang E, Saberi M (2018) An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Syst 159:120–131. https://doi.org/10.1016/j.knosys.2018.06.010

    Article  Google Scholar 

  18. Li P, Wei C (2019) International Journal of Disaster Risk Reduction An emergency decision-making method based on D-S evidence theory for probabilistic linguistic term sets. Int J Disaster Risk Reduct 37:101178. https://doi.org/10.1016/j.ijdrr.2019.101178

    Article  Google Scholar 

  19. Liu Y, Fan Z-P, Zhang Y (2014) Risk decision analysis in emergency response: A method based on cumulative prospect theory. Comput Oper Res 42:75–82. https://doi.org/10.1016/j.cor.2012.08.008

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu B, Zhao X, Li Y (2016) Review and prospect of studies on emergency management. Procedia Eng 145:1501–1508. https://doi.org/10.1016/j.proeng.2016.04.189

    Article  Google Scholar 

  21. Levy JK, Taji K (2007) Group decision support for hazards planning and emergency management: A Group Analytic Network Process (GANP) approach. Math Comput Model 46:906–917. https://doi.org/10.1016/j.mcm.2007.03.001

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhou L, Wu X, Xu Z, Fujita H (2018) Emergency decision making for natural disasters: An overview. Int. J Disaster Risk Reduct 27:567–576. https://doi.org/10.1016/j.ijdrr.2017.09.037

    Article  Google Scholar 

  23. Zhang Z-X, Wang L, Wang Y-M (2018) An emergency decision making method based on prospect theory for different emergency situations. Int J Disaster Risk Sci 9:407–420. https://doi.org/10.1007/s13753-018-0173-x

    Article  Google Scholar 

  24. Zadeh LA (1975) The Concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci (Ny) 249

  25. Yazdi M (2019) A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis. Int J Syst Assur Eng Manag 10:1–18. https://doi.org/10.1007/s13198-018-00757-7

    Article  Google Scholar 

  26. Ding J, Cai J, Guo G, Chen C (2018) An Emergency Decision-Making Method for Urban Rainstorm Water-Logging: A China Study. Sustainability. https://doi.org/10.3390/su10103453

    Article  Google Scholar 

  27. Chen M, Dong Z, Jia W, Ni X, Yao H (2019) Multi-objective joint optimal operation of reservoir system and analysis of objectives competition mechanism: a case study in the upper reach of the. Water Artic. https://doi.org/10.3390/w11122542

    Article  Google Scholar 

  28. Yazdi M (2019) A perceptual computing–based method to prioritize intervention actions in the probabilistic risk assessment techniques. Qual Reliab Eng Int. https://doi.org/10.1002/qre.2566

    Article  Google Scholar 

  29. Yazdi M (2019) Introducing a heuristic approach to enhance the reliability of system safety assessment. Qual Reliab Eng Int. https://doi.org/10.1002/qre.2545

    Article  Google Scholar 

  30. Yazdi M, Golilarz NA, Nedjati A et al (2021) An improved lasso regression model for evaluating the efficiency of intervention actions in a system reliability analysis. Neural Comput & Applic. https://doi.org/10.1007/s00521-020-05537-8

  31. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega (United Kingdom) 53:49–57. https://doi.org/10.1016/j.omega.2014.11.009

    Article  Google Scholar 

  32. Zadeh LA (2011) A Note on Z-numbers. Inf Sci (Ny) 181:2923–2932. https://doi.org/10.1016/j.ins.2011.02.022

    Article  MATH  Google Scholar 

  33. Chen Y, Larbani M (2006) Two-person zero-sum game approach for fuzzy multiple attribute decision making problems. Fuzzy Sets Syst 157:34–51. https://doi.org/10.1016/j.fss.2005.06.004

    Article  MathSciNet  MATH  Google Scholar 

  34. Mohammadi M, Rezaei J (2019) Bayesian best-worst method: A probabilistic group decision making model. Omega. https://doi.org/10.1016/j.omega.2019.06.001

    Article  Google Scholar 

  35. Mohsen O, Fereshteh N (2017) An extended VIKOR method based on entropy measure for the failure modes risk assessment–A case study of the geothermal power plant (GPP). Saf Sci 92:160–172

    Article  Google Scholar 

  36. Aliev RA, Alizadeh AV, Huseynov OH (2015) The arithmetic of discrete Z-numbers. Inf Sci (Ny) 290:134–155. https://doi.org/10.1016/j.ins.2014.08.024

    Article  MathSciNet  MATH  Google Scholar 

  37. Kang B, Deng Y, Hewage K, Sadiq R (2019) A Method of Measuring Uncertainty for Z-Number. IEEE Trans FUZZY Syst 27:731–738

    Article  Google Scholar 

  38. Zadeh LA (2015) Fuzzy logic-A personal perspective. Fuzzy Sets Syst 281:4–20. https://doi.org/10.1016/j.fss.2015.05.009

    Article  MathSciNet  MATH  Google Scholar 

  39. Koca Y, Muge O (2018) Solving two-player zero sum games with fuzzy payoffs when players have different risk attitudes. Qual Reliab Eng Int 34(7):1461–1474. https://doi.org/10.1002/qre.2322

    Article  Google Scholar 

  40. Xu J, Dong JY, Wan SP, Gao J (2019) Multiple attribute decision making with triangular intuitionistic fuzzy numbers based on zero-sum game approach. Iran J Fuzzy Syst 16:97–112

    MathSciNet  MATH  Google Scholar 

  41. Frigout J, Tasseel-ponche S, Delafontaine A (2020) Strategy and Decision Making in Karate. Front Psychol 10:1–9. https://doi.org/10.3389/fpsyg.2019.03025

    Article  Google Scholar 

  42. Zavadskas EK, Govindan K, Antucheviciene J, Turskis Z (2016) Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Econ Res Istraživanja 29:857–887. https://doi.org/10.1080/1331677X.2016.1237302

    Article  Google Scholar 

  43. Yazdi M, Khan F, Abbassi R, Rusli R (2020) Improved DEMATEL methodology for effective safety management decision-making. Saf Sci 127:104705. https://doi.org/10.1016/j.ssci.2020.104705

    Article  Google Scholar 

  44. Ibáñez-Forés V, Bovea MD, Pérez-Belis V (2014) A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. J Clean Prod 70:259–281. https://doi.org/10.1016/j.jclepro.2014.01.082

    Article  Google Scholar 

  45. Govindan K, Rajendran S, Sarkis J, Murugesan P (2015) Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J Clean Prod 98:66–83. https://doi.org/10.1016/j.jclepro.2013.06.046

    Article  Google Scholar 

  46. Yazdi M, Golilarz NA, Adesina KA, Nedjati A (2021) Probabilistic Probabilistic risk analysis of process systems considering epistemic and aleatory uncertainties: a comparison study. Int J Uncertainty Fuzziness Knowledge-Based Syst. https://doi.org/10.1142/S0218488521500098

    Article  MathSciNet  Google Scholar 

  47. Ingwersen W, Cabezas H, Weisbrod AV, Eason T, Demeke B, Ma X, Hawkins TR, Lee S-J, Bare JC, Ceja M (2014) Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability. Sustain. https://doi.org/10.3390/su6031386

    Article  Google Scholar 

  48. Yazdi M, Khan F, Abbassi R (2021) Microbiologically influenced corrosion (MIC) management using Bayesian inference, Ocean Eng. https://doi.org/10.1016/j.oceaneng.2021.108852.

  49. Rezaei J (2016) Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega (United Kingdom) 64:126–130. https://doi.org/10.1016/j.omega.2015.12.001

    Article  Google Scholar 

  50. Yazdi M, Saner T, Darvishmotevali M (2020) Application of an Artificial Intelligence Decision-Making Method for the Selection of Maintenance Strategy, in: 10th Int. Conf. Theory Appl. Soft Comput. Comput. with Words Perceptions-ICSCCW-2019. ICSCCW 2019. Adv. Intell. Syst. Comput., Springer, Cham, 2020: pp. 246–253.https://doi.org/10.1007/978-3-030-35249-3_31.

  51. Yazdi M (2019) Ignorance-aware safety and reliability analysis: A heuristic approach. Qual Reliab Eng Int 36:652–674. https://doi.org/10.1002/qre.2597

    Article  MathSciNet  Google Scholar 

  52. Mahdiraji HA, Arzaghi S, Stauskis G, Zavadskas EK (2018) A hybrid fuzzy BWM-COPRAS method for analyzing key factors of sustainable architecture. Sustain 10:1–26. https://doi.org/10.3390/su10051626

    Article  Google Scholar 

  53. Yadav G, Mangla SK, Luthra S, Jakhar S (2018) Hybrid BWM-ELECTRE-based decision framework for effective offshore outsourcing adoption: a case study. Int J Prod Res 56:6259–6278. https://doi.org/10.1080/00207543.2018.1472406

    Article  Google Scholar 

  54. Aboutorab H, Saberi M, Asadabadi MR, Hussain O, Chang E (2018) ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Syst Appl 107:115–125. https://doi.org/10.1016/j.eswa.2018.04.015

    Article  Google Scholar 

  55. Yazdi M, Korhan O, Daneshvar S (2018) Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in process industry. Int J Occup Saf Ergon. https://doi.org/10.1080/10803548.2018.1454636

    Article  Google Scholar 

  56. Yazdi M (2018) Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach. Saf Sci 110:438–448. https://doi.org/10.1016/j.ssci.2018.03.005

    Article  Google Scholar 

  57. Yazdi M (2019) Acquiring and Sharing Tacit Knowledge in Failure Diagnosis Analysis Using Intuitionistic and Pythagorean Assessments. J Fail Anal Prev 19:369–386. https://doi.org/10.1007/s11668-019-00599-w

    Article  Google Scholar 

  58. Yazdi M, Daneshvar S, Setareh H (2017) An extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) application for aircraft landing system. Safe Sci 98113-123. https://doi.org/10.1016/j.ssci.2017.06.009

  59. Yazdi M, Nikfar F, Nasrabadi M (2017) Failure probability analysis by employing fuzzy fault tree analysis. Int J Syst Assur Eng Manag 8:1177–1193. https://doi.org/10.1007/s13198-017-0583-y

    Article  Google Scholar 

  60. Yazdi M (2018) Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-018-0496-2

    Article  Google Scholar 

  61. Deng X, Jiang W, Wang Z (2019) Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution. Appl Math Comput 340:101–112. https://doi.org/10.1016/j.amc.2018.08.032

    Article  MathSciNet  MATH  Google Scholar 

  62. Ma J, Zheng Y, Wu B, Wang L (2016) Automatica Equilibrium topology of multi-agent systems with two leaders. Automatica 73:200–206. https://doi.org/10.1016/j.automatica.2016.07.005

    Article  MATH  Google Scholar 

  63. Binmore K (2007) Playing for real: a text on game theory. Oxford University Press, New York

    Book  Google Scholar 

  64. Neumann V (1959) On the theory of games of strategy, Contrib Theory Games 13–42.

  65. Yazdi M, Adesina KA, Korhan O, Nikfar F (2019) Learning from Fire Accident at Bouali Sina Petrochemical Complex Plant. J Fail Anal Prev. https://doi.org/10.1007/s11668-019-00769-w

    Article  Google Scholar 

  66. Rausand M, Haugen S (2020) Risk Assessment: Theory, Methods, and Applications. Wiley, NY

    Book  Google Scholar 

  67. Iran International, Unprecedented Flood in North of Iran, (2019).

  68. Ardalan A, Naieni KH, Kabir M (2009) Evaluation of Golestan Province ’ s Early Warning System for flash floods, Iran 2006–7. Int J Biometeorol. https://doi.org/10.1007/s00484-009-0210-y

    Article  Google Scholar 

  69. Yazdi M, Hafezi P, Abbassi R (2019) A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment. J Loss Prev Process Ind. https://doi.org/10.1016/j.jlp.2019.02.001

    Article  Google Scholar 

  70. Saltelli A (2002) Sensitivity analysis for importance assessment. Risk Anal. Wiley, NY, pp 579–590

    Google Scholar 

  71. Aliev RA, Pedrycz W, Huseynov OH (2018) Functions defined on a set of Z-numbers. Inf Sci (Ny) 423:353–375. https://doi.org/10.1016/j.ins.2017.09.056

    Article  MathSciNet  MATH  Google Scholar 

  72. Yousefzadeh S, Yaghmaeian K, Mahvi AH, Nasseri S, Alavi N, Nabizadeh R (2020) Comparative analysis of hydrometallurgical methods for the recovery of Cu from circuit boards: Optimization using response surface and selection of the best technique by two-step fuzzy AHP-TOPSIS method. J Clean Prod 249:119401. https://doi.org/10.1016/j.jclepro.2019.119401

    Article  Google Scholar 

  73. Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36:11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039

    Article  Google Scholar 

  74. Gupta H, Barua MK (2018) A framework to overcome barriers to green innovation in SMEs using BWM and Fuzzy TOPSIS. Sci Total Environ 633:122–139. https://doi.org/10.1016/j.scitotenv.2018.03.173

    Article  Google Scholar 

  75. Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39:13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056

    Article  Google Scholar 

  76. Zhang SC, Wang H, Liu Z, Zeng S, Jin Y, Baležentis T (2019) A comprehensive evaluation of the community environment adaptability for elderly people based on the improved TOPSIS. Inf. https://doi.org/10.3390/info10120389

    Article  Google Scholar 

  77. Wood DA (2016) Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. J Nat Gas Sci Eng 28:594–612. https://doi.org/10.1016/j.jngse.2015.12.021

    Article  Google Scholar 

  78. Sharma S, Balan S (2013) An integrative supplier selection model using Taguchi loss function, TOPSIS and multi criteria goal programming. J Intell Manuf 24:1123–1130. https://doi.org/10.1007/s10845-012-0640-y

    Article  Google Scholar 

  79. Ouyang M (2014) Review on modeling and simulation of interdependent critical infrastructure systems. Reliab Eng Syst Saf 121:43–60. https://doi.org/10.1016/j.ress.2013.06.040

    Article  Google Scholar 

  80. Yu L, Keung K (2011) Multi-criteria emergency decision support. Decis Support Syst 51:307–315. https://doi.org/10.1016/j.dss.2010.11.024

    Article  Google Scholar 

  81. Xu X, Du Z, Chen X (2015) Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions. Decis Support Syst 79:150–160. https://doi.org/10.1016/j.dss.2015.08.009

    Article  Google Scholar 

  82. Boehm C, Antweiler C, Kent S, Knauft M, Mithen S, Richerson PJ, Wilson DS, Boehm C (1996) Emergency Decisions, Cultural- Selection Mechanics, and Group Selection ’. Curr Anthropol 37:763–793

    Article  Google Scholar 

  83. Peng X, Garg H (2018) Computers & Industrial Engineering Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput Ind Eng 119:439–452. https://doi.org/10.1016/j.cie.2018.04.001

    Article  Google Scholar 

  84. Dominey-howes D, Minos-minopoulos D (2004) Perceptions of hazard and risk on Santorini. J Volcanol Geotherm Res 137:285–310. https://doi.org/10.1016/j.jvolgeores.2004.06.002

    Article  Google Scholar 

  85. Wang L, Zhang Z, Wang Y (2015) A prospect theory-based interval dynamic reference point method for emergency decision making. Expert Syst Appl 42:9379–9388. https://doi.org/10.1016/j.eswa.2015.07.056

    Article  Google Scholar 

  86. Leonard GS, Johnston DM, Paton D, Christianson A, Becker J, Keys H (2008) Developing effective warning systems: Ongoing research at Ruapehu volcano, New Zealand. J Volcanol Geotherm Res 172:199–215. https://doi.org/10.1016/j.jvolgeores.2007.12.008

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by Scientific Research Starting Project of SWPU under Grant No. 2019QHZ007. The first author has been supported by the scholarship from China Scholarship Council (CSC) under Grant No. 201806070048.

Author information

Authors and Affiliations

  1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, 510275, People’s Republic of China

    He Li & Mohammad Yazdi

  2. School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, Sichuan, 610500, People’s Republic of China

    Jun-Yu Guo

  3. Industrial Engineering Department, Quchan University of Technology, Quchan, Iran

    Arman Nedjati

  4. Industrial Engineering Department, Near East University, KKTC, Nicosia, North Cyprus, Turkey

    Kehinde Adewale Adesina

Authors
  1. He Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jun-Yu Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mohammad Yazdi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Arman Nedjati
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Kehinde Adewale Adesina
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jun-Yu Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no known conflict of interest statement or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Guo, JY., Yazdi, M. et al. Supportive emergency decision-making model towards sustainable development with fuzzy expert system. Neural Comput & Applic 33, 15619–15637 (2021). https://doi.org/10.1007/s00521-021-06183-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06183-4

Keywords