References

1. S. Goswami, S. Chakraborty, S. Ghosh, A. Chakrabarti, B. Chakraborty, A review on application of data mining techniques to combat natural disasters, Ain Shams Eng. J., 9 (2018) 365–378.
2. C. Kousky, Informing climate adaptation: a review of the economic costs of natural disasters, Energy Econ., 46 (2014) 576–592.
3. J. Xu, Z. Wang, F. Shen, C. Ouyang, Y. Tu, Natural disasters, and social conflict: a systematic literature review, Int. J. Disaster Risk Reduct., 17 (2016) 38–48.
4. S. Castiglioni, E. Davoli, F. Riva, M. Palmiotto, P. Camporini, A. Manenti, E. Zuccato, Mass balance of emerging contaminants in the water cycle of a highly urbanized and industrialized area of Italy, Water Res., 131 (2018) 287–298.
5. Q. Wu, H. Zhou, N.F.Y. Tam, Y. Tian, Y. Tan, S. Zhou, Q. Li, Y. Chen, J.Y.S. Leung, Contamination, toxicity and speciation of heavy metals in an industrialized urban river: implications for the dispersal of heavy metals, Mar. Pollut. Bull., 104 (2016) 153–161.
6. L.T. Di Gregorio, C.A.P. Soares, Post-disaster housing recovery guidelines for development countries based on experiences in the American continent, Int. J. Disaster Risk Reduct., 24 (2017) 340–347.
7. J. Mendonça, E. Andrade, P.T. Endo, R. Lima, Disaster recovery solutions for IT systems: a systematic mapping study, J. Syst. Software, 149 (2019) 511–530.
8. W.B.G. Fernando, A.H. Gunapala, W.A. Jayantha, Water supply and sanitation needs in a disaster – lessons learned through the tsunami disaster in Sri Lanka, Desalination, 248 (2009) 14–21.
9. M. Sabbaghtorkan, R. Batta, Q. He, Prepositioning of assets and supplies in disaster operations management: review and research gap identification, Eur. J. Oper. Res., 284 (2020) 1–19.
10. F. Yu, X.-Y. Li, X.-S. Han, Risk response for urban water supply network using case-based reasoning during a natural disaster, Saf. Sci., 106 (2018) 121–139.
11. M. Ozcelik, Alternative model for electricity and water supply after disaster, J. Taibah Univ. Sci., 11 (2017) 966–974.
12. L. Zhou, X. Wu, Z. Xu, H. Fujita, Emergency decision-making for natural disasters: an overview, Int. J. Disaster Risk Reduct., 27 (2018) 567–576.
13. D.A. Rose, S. Murthy, J. Brooks, J. Bryant, The evolution of public health emergency management as a field of practice, Am. J. Public Health, 107 (2017) S126–S133.
14. K.T. Ton, J.C. Gaillard, C.E. Adamson, C. Akgungor, H.T. Ho, Expanding the capabilities of people with disabilities in disaster risk reduction, Int. J. Disaster Risk Reduct., 34 (2019) 11–17.
15. A. Damalas, C. Mettas, E. Evagorou, S. Giannecchini, C. Iasio, M. Papadopoulos, A. Konstantinou, D. Hadjimitsis, Development and implementation of a DECATASTROPHIZE platform and tool for the management of disasters or multiple hazards, Int. J. Disaster Risk Reduct., 31 (2018) 589–601.
16. D. Fogli, G. Guida, Knowledge-centered design of decision support systems for emergency management, Decis. Support Syst., 55 (2013) 336–347.
17. J. Zhang, H. Liu, G. Yu, J. Ruan, F.T.S. Chan, A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics, Comput. Ind. Eng., 135 (2019) 1145–1154.
18. S.A. Bagloee, K.H. Johansson, M. Asadi, A hybrid machinelearning and optimization method for contraflow design in post-disaster cases and traffic management scenarios, Expert Syst. Appl., 124 (2019) 67–81.
19. D. Sarma, A. Das, U.K. Bera, I.M. Hezam, Redistribution for cost minimization in disaster management under uncertainty with trapezoidal neutrosophic number, Comput. Ind., 109 (2019) 226–238.
20. M. Dorasamy, M. Raman, M. Kaliannan, Integrated community emergency management and awareness system: a knowledge management system for disaster support, Technol. Forecasting Social Change, 121 (2017) 139–167.
21. Y. Shahtaheri, M.M. Flint, J.M. de la Garza, A multi-objective reliability-based decision support system for incorporating decision-maker utilities in the design of infrastructure, Adv. Eng. Inf., 42 (2019) 100939.
22. M.S. Khorshidi, M.R. Nikoo, E. Ebrahimi, M. Sadegh, A robust decision support leader-follower framework for design of contamination warning system in water distribution network, J. Cleaner Prod., 214 (2019) 666–673.
23. A. Tufano, R. Accorsi, F. Garbellini, R. Manzini, Plant design and control in food service industry, A multi-disciplinary decision-support system, Comput. Ind., 103 (2018) 72–85.
24. X. Zhao, S. Bai, X. Zhang, Establishing a decision-support system for eco-design of biological wastewater treatment: a case study of bioaugmented constructed wetland, Bioresour. Technol., 274 (2019) 425–429.
25. E. Kuznetsova, M.-A. Cardin, M. Diao, S. Zhang, Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design, Renewable Sustainable Energy Rev., 103 (2019) 477–500.
26. S.M. Ghavami, Multi-criteria spatial decision support system for identifying strategic roads in disaster situations, Int. J. Crit. Infrastruct. Prot., 24 (2019) 23–36.
27. A. Boggia, G. Massei, E. Pace, L. Rocchi, L. Paolotti, M. Attard, Spatial multicriteria analysis for sustainability assessment: a new model for decision-making, Land Use Policy, 71 (2018) 281–292.
28. A. Mardani, E.K. Zavadskas, Z. Khalifah, N. Zakuan, A. Jusoh, K.M. Nor, M. Khoshnoudi, A review of multi-criteria decisionmaking applications to solve energy management problems: two decades from 1995 to 2015, Renewable Sustainable Energy Rev., 71 (2017) 216–256.
29. M. Dell’Ovo, S. Capolongo, A. Oppio, Combining spatial analysis with MCDA for the siting of healthcare facilities, Land Use Policy, 76 (2018) 634–644.
30. O.E. Demesouka, A.P. Vavatsikos, K.P. Anagnostopoulos, Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: method, implementation, and case study, Waste Manage., 33 (2013) 1190–1206.
31. D. Delgado-Gomez, E. Baca-Garcia, D. Aguado, P. Courtet, J. Lopez-Castroman, Computerized adaptive test vs. decision trees: development of a support decision system to identify suicidal behavior, J. Affective Disord., 206 (2016) 204–209.
32. H.-H. Yu, K.-H. Chang, H.-W. Hsu, R. Cuckler, A Monte Carlo simulation-based decision support system for reliability analysis of Taiwan’s power system: framework and empirical study, Energy, 178 (2019) 252–262.
33. D. Zheng, L. Yu, L. Wang, A techno-economic-risk decisionmaking methodology for large-scale building energy efficiency retrofit using Monte Carlo simulation, Energy, 189 (2019) 116169.
34. A.M. Mutawa, M.A. Alzuwawi, Multilayered rule-based expert system for diagnosing uveitis, Artif. Intell. Med., 99 (2019) 101691.
35. S. Qiu, M. Sallak, W. Schön, H.X.G. Ming, A valuation-based system approach for risk assessment of belief rule-based expert systems, Inf. Sci., 466 (2018) 323–336.
36. EPA, Planning for an Emergency Drinking Water Supply, Environmental Protection Agency, 2011.
37. P.H. Dos Santos, S.M. Neves, D.O. Sant’Anna, C.H.d. Oliveira, H.D. Carvalho, The analytic hierarchy process supporting decision-making for sustainable development: an overview of applications, J. Cleaner Prod., 212 (2019) 119–138.
38. S.G. Arcidiacono, S. Corrente, S. Greco, GAIA-SMAAPROMETHEE for a hierarchy of interacting criteria, Eur. J. Oper. Res., 270 (2018) 606–624.
39. M.A. Boujelben, A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering, Omega, 69 (2017) 126–140.
40. Available at: http://www.promethee-gaia.net/index.html
41. B. Mareschal, Visual PROMETHEE 1.4 manual, Vol. 1, Visual PROMETHEE 1, Framingham, MA, 2013.
42. J. Jung, H.-R. Cho, J. Sohn, S. Lee, S.K. Chae, An experimental study on decision-making for multi-source water, J. Korea Soc. Water Wastewater, 29 (2015) 1–9.
43. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining software: an update, ACM SIGKDD Explor. Newsl., 11 (2009) 10–18.
44. http://www.exsys.com/exsyscorvid.html
45. MEK, Manual of Response to the Drinking Water Crisis Response Manual, Ministry of Environment in Korea, 2016.