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Robust Flood Risk Management Strategies Through Bayesian Estimation and Multi-objective Optimization

Abstract

Management and control of flood hazards, the most frequent natural disaster worldwide, has become a greater challenge due to the increasingly unpredictable precipitation and runoff due to climate change. As many rural areas in Iran are vulnerable to flash floods occurring mainly in the spring, more accurate plans are needed to help reduce the risk of related damage. To address this concern, a robust methodology using multi-objective optimization is proposed, which incorporates the large uncertainties in the modeling parameters defining the risk of flooding. The proposed framework has been implemented in the upper catchment of the Taleghanrood river in the Taleghan district in Iran, which is vulnerable to flooding. The results provide a detailed performance assessment of alternative infrastructure designs, which will help to increase the efficiency of flood management strategies. The optimization uses multi-criteria optimization evolutionary algorithms (MOEA) and Bayesian estimation concepts. The resulting specific design plans, as levees’ height increases over a 50-year time horizon, for controlling floods under given scenarios reflect the uncertainty in the parameters.

Graphic Abstract

Article Highlights

  • A robust decision making model has been developed to address flood management scenarios.

  • The proposed methodology addresses deep uncertainties in decision parameters.

  • Sensitivity analysis of the plausible scenarios and discovers vulnerable scenarios has been done.

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adapted from Hosseini et al. 2012)

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All data generated or analyzed during this study are included in this published article.

References

  1. Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Sci Total Environ 750:141565

    CAS  Article  Google Scholar 

  2. Borgomeo E, Mortazavi-naeini M, Hall J (2018) Risk, robustness and water resources management under uncertainty. February. https://doi.org/10.1002/eft2.299

  3. Capano G, Woo JJ (2018) Designing policy robustness: outputs and processes. Policy Soc 37(4):422–440. https://doi.org/10.1080/14494035.2018.1504494

    Article  Google Scholar 

  4. Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin. https://doi.org/10.1007/978-0-387-36797-2

    Book  Google Scholar 

  5. de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazard 16(4):1019–1033

    Article  Google Scholar 

  6. de Moel H, Aerts JCJH (2011) Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates. Nat Hazards 58(1):407–425. https://doi.org/10.1007/s11069-010-9675-6

    Article  Google Scholar 

  7. Department of Energy (2009) Iran Water Resources Management Company, Tehran Regional Water Company organizing the surface waters in south of Tehran (qualitative and quantitative studies). Volume I: report of weather report code: TWM/MS-02 (in farsi)

  8. Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1–2):24–49. https://doi.org/10.1016/S0022-1694(03)00084-2

    Article  Google Scholar 

  9. Eijgenraam C, Brekelmans R, Den Hertog D, Roos K (2017) Optimal strategies for flood prevention. Manage Sci 63(5):1644–1656. https://doi.org/10.1287/mnsc.2015.2395

    Article  Google Scholar 

  10. Esmaeelzadeh R, Dariane AB (2014) Long-term streamflow forecasting by adaptive neuro-fuzzy inference system using K-fold cross-validation: case study. Taleghan Basin, Iran

  11. Goharnejad H, Azizkhani M, Niri MZ, Moazami S (2017) Investigating the vulnerability downstream area of Taleghan dam due to dam failure. J Hydraul Struct 3(2):1–9. https://doi.org/10.22055/jhs.2017.13435

    Article  Google Scholar 

  12. Gschnitzer T, Gems B, Mazzorana B, Aufleger M (2017) Towards a robust assessment of bridge clogging processes in flood risk management. Geomorphology 279:128–140

    Article  Google Scholar 

  13. Hadjimichael A, Gold D, Hadka D, Reed P (2020) Rhodium: python library for many-objective robust decision making and exploratory modeling. J Open Res Softw. https://doi.org/10.5334/JORS.293

    Article  Google Scholar 

  14. Hadka D, Reed P (2013) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput 21(2):231–259. https://doi.org/10.1162/EVCO_a_00075

    Article  Google Scholar 

  15. Hadka D, Herman J, Reed P, Keller K (2015) An open-source framework for many-objective robust decision making. Environ Model Softw 74:114–129. https://doi.org/10.1016/j.envsoft.2015.07.014

    Article  Google Scholar 

  16. Herman JD, Reed PM, Zeff HB, Characklis GW (2015) How should robustness be defined for water systems planning under change? J Water Resour Plan Manage 141(10):04015012. https://doi.org/10.1061/(asce)wr.1943-5452.0000509

    Article  Google Scholar 

  17. 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 Change 3(9):816–821. https://doi.org/10.1038/nclimate1911

    Article  Google Scholar 

  18. Hosseini M, Ghafouri AM, Amin MSM, Tabatabaei MR, Goodarzi M, Abde Kolahchi A (2012) Effects of land use changes on water balance in Taleghan catchment, Iran. J Agric Sci Technol 14(5):1159–1172

    Google Scholar 

  19. Keller AA (2019) Multi-objective optimization in theory and practice II: metaheuristic algorithms. Bentham Science Publishers, Sharjah. https://doi.org/10.2174/97816810870541190101

    Book  Google Scholar 

  20. Lempert RJ (2002) A new decision science for complex systems. Proc Natl Acad Sci USA 99(suppl 3):7309–7313. https://doi.org/10.1073/pnas.082081699

    CAS  Article  Google Scholar 

  21. Maier HR, Kapelan Z, Kasprzyk J, Kollat J, Matott LS, Cunha MC, Dandy GC, Gibbs MS, Keedwell E, Marchi A, Ostfeld A, Savic D, Solomatine DP, Vrugt JA, Zecchin AC, Minsker BS, Barbour EJ (2014) Environmental modelling and software evolutionary algorithms and other metaheuristics in water resources : current status, research challenges and future directions. Environ Model Softw 62:271–299. https://doi.org/10.1016/j.envsoft.2014.09.013

    Article  Google Scholar 

  22. Merwade V, Olivera F, Arabi M, Edleman S (2008) Uncertainty in flood inundation mapping: current issues and future directions. J Hydrol Eng 13(7):608–620. https://doi.org/10.1061/(asce)1084-0699(2008)13:7(608)

    Article  Google Scholar 

  23. Meysami R, Niksokhan MH (2020) Evaluating robustness of waste load allocation under climate change using multi-objective decision making. J Hydrol 588:125091. https://doi.org/10.1016/j.jhydrol.2020.125091

    CAS  Article  Google Scholar 

  24. Moallemi EA, Elsawah S, Ryan MJ (2020) Robust decision making and Epoch-Era analysis: a comparison of two robustness frameworks for decision-making under uncertainty. Technol Forecast Soc Change 151:119797. https://doi.org/10.1016/j.techfore.2019.119797

    Article  Google Scholar 

  25. Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld A, Singh A, Zechman E (2010) State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resour Plan Manage 136(4):412–432. https://doi.org/10.1061/(asce)wr.1943-5452.0000053

    Article  Google Scholar 

  26. Plate EJ (2002) Flood risk and flood management. J Hydrol 267(1–2):2–11. https://doi.org/10.1016/S0022-1694(02)00135-X

    Article  Google Scholar 

  27. Razavi S, Jakeman A, Saltelli A, Prieur C, Iooss B, Borgonovo E, Plischke E, Lo Piano S, Iwanaga T, Becker W, Tarantola S, Guillaume JHA, Jakeman J, Gupta H, Melillo N, Rabitti G, Chabridon V, Duan Q, Sun X, Maier HR (2021) The future of sensitivity analysis: an essential discipline for systems modeling and policy support. Environ Model Softw 137:104954. https://doi.org/10.1016/j.envsoft.2020.104954

    Article  Google Scholar 

  28. Reed MS, Kenter J, Bonn A, Broad K, Burt TP, Fazey IR, Fraser EDG, Hubacek K, Nainggolan D, Quinn CH, Stringer LC, Ravera F (2013a) Participatory scenario development for environmental management: a methodological framework illustrated with experience from the UK uplands. J Environ Manage 128:345–362. https://doi.org/10.1016/j.jenvman.2013.05.016

    CAS  Article  Google Scholar 

  29. Reed PM, Hadka D, Herman J, Kasprzyk J, Kollat J (2013b) Evolutionary multi-objective optimization in water resources: the past, present, and future. Adv Water Resour 51:438–456

    Article  Google Scholar 

  30. Smith KR, Woodward A, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, Olwoch JM, Revich B, Sauerborn R (2014) Human health: impacts, adaptation, and co-benefits. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change

  31. Takbiri Z, Afshar A (2012) Multi-objective optimization of fuse gates system under hydrologic uncertainties. Water Resour Manage 26(8):2323–2345. https://doi.org/10.1007/s11269-012-0019-z

    Article  Google Scholar 

  32. van Dantzig D (1956) Economic decision problems for flood prevention. Econometrica 24(3):276. https://doi.org/10.2307/1911632

    Article  Google Scholar 

  33. Wu W, Emerton R, Duan Q, Wood AW, Wetterhall F, Robertson DE (2020) Ensemble flood forecasting: current status and future opportunities. Wires Water 7(3):1–32. https://doi.org/10.1002/wat2.1432

    Article  Google Scholar 

  34. Yoosefdoost A, Yoosefdoost I, Asghari H, Sadegh Sadeghian M (2018) Comparison of HadCM3, CSIRO Mk3 and GFDL CM2.1 in prediction the climate change in Taleghan River Basin. Am J Civil Eng Architect 6(3):93–100. https://doi.org/10.12691/ajcea-6-3-1

    Article  Google Scholar 

  35. Zhou X, Ma W, Echizenya W, Yamazaki D (2021) The uncertainty of flood frequency analyses in hydrodynamic model simulations. Nat Hazard 21(3):1071–1085. https://doi.org/10.5194/nhess-21-1071-2021

    Article  Google Scholar 

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Correspondence to Mohammad Hossein Niksokhan.

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Sobhaniyeh, Z., Niksokhan, M.H., Omidvar, B. et al. Robust Flood Risk Management Strategies Through Bayesian Estimation and Multi-objective Optimization. Int J Environ Res 15, 1057–1070 (2021). https://doi.org/10.1007/s41742-021-00370-w

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Keywords

  • Robust optimization
  • Uncertainty analysis
  • Flood risk control
  • Deep uncertainty
  • Bayesian estimation