Water Resources Management

, Volume 32, Issue 5, pp 1615–1629 | Cite as

A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management

Article
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Abstract

Flood, as a serious worldwide environment problem, can lead to detrimental economic losses and fatalities. Effective flood control is desired to mitigate the adverse impacts of flooding and the associated flood risk through development of cost-effective and efficient flood management decisions and policies. A bi-level fuzzy two-stage stochastic programming model, named BIFS model is developed in this study to provide decision support for economic analysis of flood management. The BIFS model is capable of not only addressing the sequential decision making issue involving the two-level decision makers, but also correcting the pre-regulated flood management decisions before the occurrence of a flood event in the two-stage environment. The probabilistic and non-probabilistic uncertainties expressed as probability density functions and fuzzy sets are quantitatively analyzed. The overall satisfaction solution is obtained for meeting the goals of the two-level decision makers by compromising, reflecting the tradeoffs among various decision makers in the two decision-making levels. The results of application of the BIFS model to a representative case study indicate informed decision strategies for flood management. Tradeoffs between economic objectives and uncertainty-averse attitudes of decision makers are quantified.

Keywords

Flood management Diversion Two-level Two-stage Economic analysis 

Notes

Acknowledgements

The authors are very grateful for the insightful comments from the Editor, Associate Editor and anonymous reviewers.

References

  1. Alfieri L, Feyen L, Dottori F, Bianchi A (2015) Ensemble flood risk assessment in Europe under high end climate scenarios. Glob Environ Chang 35:199–212.  https://doi.org/10.1016/j.gloenvcha.2015.09.004 CrossRefGoogle Scholar
  2. Arora SR, Gupta R (2009) Interactive fuzzy goal programming approach for bilevel programming problem. Eur J Oper Res 194:368–376.  https://doi.org/10.1016/j.ejor.2007.12.019 CrossRefGoogle Scholar
  3. Baky IA (2009) Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems. Fuzzy Sets Syst 160:2701–2713CrossRefGoogle Scholar
  4. Baky IA, Abo-Sinna MA (2013) TOPSIS for bi-level MODM problems. Appl Math Model 37:1004–1015.  https://doi.org/10.1016/j.apm.2012.03.002 CrossRefGoogle Scholar
  5. Barredo JI (2007) Major flood disasters in Europe: 1950-2005. Nat Hazards 42:125–148.  https://doi.org/10.1007/s11069-006-9065-2 CrossRefGoogle Scholar
  6. Bellman RE, Zadeh LA (1970) Decision-Making in a Fuzzy Environment. Manag Sci 17:B141–B164CrossRefGoogle Scholar
  7. Ding Y, Wang SSY (2012) Optimal control of flood diversion in watershed using nonlinear optimization. Adv Water Resour 44:30–48.  https://doi.org/10.1016/j.advwatres.2012.04.004 CrossRefGoogle Scholar
  8. Dottori F, Salamon P, Bianchi A et al (2016) Development and evaluation of a framework for global flood hazard mapping. Adv Water Resour 94:87–102.  https://doi.org/10.1016/j.advwatres.2016.05.002 CrossRefGoogle Scholar
  9. EEA (2010) Mapping the impacts of natural hazards and technological accidents in Europe: An overview of the last decade. EES (European Environment Agency) Technical Report, No 13/2010Google Scholar
  10. Guo P, Huang GH, Li YP (2010) An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties. Adv Water Resour 33:81–91.  https://doi.org/10.1016/j.advwatres.2009.10.009 CrossRefGoogle Scholar
  11. Huang GH, Loucks DP (2000) An inexact two-stage stochastic programming model for water resources management under uncertainty. Civ Eng Environ Syst 17:95–118CrossRefGoogle Scholar
  12. Inuiguchi M, Ramik J (2000) Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst 111:3–28.  https://doi.org/10.1016/S0165-0114(98)00449-7 CrossRefGoogle Scholar
  13. Inuiguchi M, Tanino T (2000) Portfolio selection under independent possibilistic information. Fuzzy Sets Syst 115:83–92.  https://doi.org/10.1016/S0165-0114(99)00026-3 CrossRefGoogle Scholar
  14. Jongman B, Ward PJ, Aerts JCJH (2012) Global exposure to river and coastal flooding: Long term trends and changes. Glob Environ Chang 22:823–835.  https://doi.org/10.1016/j.gloenvcha.2012.07.004 CrossRefGoogle Scholar
  15. Lai YJ, Hwang CL (1992) A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst 49:121–133CrossRefGoogle Scholar
  16. Lai YJ, Hwang CL (1993) Possibilistic linear programming for managing interest rate risk. Fuzzy Sets Syst 54:135–146.  https://doi.org/10.1016/0165-0114(93)90271-I CrossRefGoogle Scholar
  17. Li YP, Huang GH, Nie SL (2007) Mixed interval-fuzzy two-stage integer programming and its application to flood-diversion planning. Eng Optim 39:163–183.  https://doi.org/10.1080/03052150601044831 CrossRefGoogle Scholar
  18. Li M, Guo P, Ren C (2015) Water Resources Management Models Based on Two-Level Linear Fractional Programming Method under Uncertainty. J Water Resour Plan Manag 141:5015001.  https://doi.org/10.1061/(ASCE)WR.1943-5452.0000518 CrossRefGoogle Scholar
  19. Liu Z, Huang G (2009) Dual-interval two-stage optimization for flood management and risk analysis. Water Resour Manag 23:2141–2162CrossRefGoogle Scholar
  20. Liu ZF, Huang GH, Li N (2008) A dynamic optimization approach for power generation planning under uncertainty. Energy Sources, Part A Recover Util Environ Eff 30:1413–1431.  https://doi.org/10.1080/15567030801929217 CrossRefGoogle Scholar
  21. Lund JR (2002) Floodplain planning with risk-based optimization. J Water Resour Plan Manag 127:202–207.  https://doi.org/10.1061/(ASCE)0733-9496(2002)128:3(202) CrossRefGoogle Scholar
  22. Maqsood I, Huang GH (2013) A dual two-stage stochastic model for flood management with inexact-integer analysis under multiple uncertainties. Stoch Environ Res Risk Assess 27:643–657.  https://doi.org/10.1007/s00477-012-0629-2 CrossRefGoogle Scholar
  23. Moitra BN, Pal BB (2002) A Fuzzy Goal Programming Approach for Solving Bilevel Programming Problems. In: Pal N, Sugeno M (eds) Advances in Soft Computing - AFSS 2002. Springer, Berlin, pp 91–98CrossRefGoogle Scholar
  24. Pramanik S, Roy TK (2007) Fuzzy goal programming approach to multilevel programming problems. Eur J Oper Res 176:1151–1166.  https://doi.org/10.1016/j.ejor.2005.08.024 CrossRefGoogle Scholar
  25. Sakawa M, Matsui T (2013) Interactive fuzzy random cooperative two-level linear programming through level sets based probability maximization. Expert Syst Appl 40:1400–1406.  https://doi.org/10.1016/j.eswa.2012.08.048 CrossRefGoogle Scholar
  26. Shih H-S, Lai Y-J, Lee ES (1996) Fuzzy approach for multi-level programming problems. Comput Oper Res 23:73–91.  https://doi.org/10.1016/0305-0548(95)00007-9 CrossRefGoogle Scholar
  27. Sinha S (2003) Fuzzy programming approach to multi-level programming problems. Fuzzy Sets Syst 136:189–202.  https://doi.org/10.1016/S0165-0114(02)00362-7 CrossRefGoogle Scholar
  28. Wang S, Huang GH (2013) A two-stage mixed-integer fuzzy programming with interval-valued membership functions approach for flood-diversion planning. J Environ Manag 117:208–218.  https://doi.org/10.1016/j.jenvman.2012.12.037 CrossRefGoogle Scholar
  29. Wang S, Huang GH, Zhou Y (2015) Inexact Probabilistic Optimization Model and Its Application to Flood Diversion Planning in a Dynamic and Uncertain Environment. J Water Resour Plan Manag 141:4014093.  https://doi.org/10.1061/(ASCE)WR.1943-5452.0000492 CrossRefGoogle Scholar
  30. Xu Y, Huang G, Fan Y (2015) Multivariate flood risk analysis for Wei River. Stoch Environ Res Risk Assess.  https://doi.org/10.1007/s00477-015-1196-0
  31. Zhang XD, Huang G (2014) Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment. J Environ Manag 135:11–18.  https://doi.org/10.1016/j.jenvman.2014.01.014 CrossRefGoogle Scholar
  32. Zhang XD, Vesselinov VV (2016) Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Appl Energy 183:77–87.  https://doi.org/10.1016/j.apenergy.2016.08.156 CrossRefGoogle Scholar
  33. Zhang XD, Huang GH, Nie XH (2009) Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty. Sci Total Environ 408:192–201CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Environmental Science and EngineeringSouth University of Science and Technology of ChinaShenzhenPeople’s Republic of China
  2. 2.Engineering Innovation Center (Beijing)South University of Science and Technology of ChinaBeijingPeople’s Republic of China
  3. 3.Earth and Environmental Sciences Division, Los Alamos National LaboratoryLos AlamosUSA

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