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Joint Monte Carlo and possibilistic simulation for flood damage assessment

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Abstract

A joint Monte Carlo and fuzzy possibilistic simulation (MC-FPS) approach was proposed for flood risk assessment. Monte Carlo simulation was used to evaluate parameter uncertainties associated with inundation modeling, and fuzzy vertex analysis was applied for promulgating human-induced uncertainty in flood damage estimation. A study case was selected to show how to apply the proposed method. The results indicate that the outputs from MC-FPS would present as fuzzy flood damage estimate and probabilistic-possibilistic damage contour maps. The stochastic uncertainty in the flood inundation model and fuzziness in the depth-damage functions derivation would cause similar levels of influence on the final flood damage estimate. Under the worst scenario (i.e. a combined probabilistic and possibilistic uncertainty), the estimated flood damage could be 2.4 times higher than that computed from conventional deterministic approach; considering only the pure stochastic effect, the flood loss would be 1.4 times higher. It was also indicated that uncertainty in the flood inundation modeling has a major influence on the standard deviation of the simulated damage, and that in the damage-depth function has more notable impact on the mean of the fitted distributions. Through applying MC-FPS, rich information could be derived under various α-cut levels and cumulative probabilities, and it forms an important basis for supporting rational decision making for flood risk management under complex uncertainties.

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Acknowledgments

This research was supported by Singapore’s Ministry of Education (MOM) AcRF Tier 1 Project (M4010973.030) and Earth Observatory of Singapore Project (M4430004.B50). The study also received support from DHI Water & Environment (S) Pte. Ltd. and DHI-NTU Water and Environment Research Centre and Education Hub, Singapore. The authors are deeply grateful to Dr. Paul Bates (University of Bristol) for providing the LISFLOOD-FP model and the relevant test data.

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Authors and Affiliations

  1. School of Civil & Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    J. J. Yu & X. S. Qin

  2. DHI-NTU Water & Environment Research Centre and Education Hub, DHI Water & Environment (S) Pte. Ltd., 1 CleanTech Loop, #03-05, CleanTech One, Singapore, 637141, Singapore

    J. J. Yu & O. Larsen

  3. Earth Observatory of Singapore (EOS), Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    X. S. Qin

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  1. J. J. Yu
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  2. X. S. Qin
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Correspondence to X. S. Qin.

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Yu, J.J., Qin, X.S. & Larsen, O. Joint Monte Carlo and possibilistic simulation for flood damage assessment. Stoch Environ Res Risk Assess 27, 725–735 (2013). https://doi.org/10.1007/s00477-012-0635-4

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