Joint Monte Carlo and possibilistic simulation for flood damage assessment
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ORIGINAL PAPER
Joint Monte Carlo and possibilistic simulation for flood damage assessment J. J. Yu • X. S. Qin • O. Larsen
Published online: 19 August 2012 Ó Springer-Verlag 2012
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
J. J. Yu X. S. Qin (&) School of Civil & Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore e-mail: [email protected] J. J. Yu e-mail: [email protected] J. J. Yu O. Larsen 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 e-mail: [email protected] X. S. Qin Earth Observatory of Singapore (EOS), Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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 MCFPS, rich information could be derived under various a-cut levels and cumulative probabilities, and it forms an important basis for supporting rational decision making for flood risk management under complex uncertainties. Keywords Monte Carlo Fuzzy vertex Flood damage Flood inundation model Depth-damage function Uncertainty
1 Introduction Evaluation of flood damage is essential for aiding decisionmaking in urban planning and development, flood disaster mitigation, and emergency management. In most cases, the flood damage is a deterministic value estimated from projecting the inundated water depth and applying the depthdamage function for a specific flood event. The uncertainties associated with both the flood inundation modeling and damage data survey and evaluation process are normally neglected, which unavoidably under- or over-estimate the final damage evaluation (Li et al. 2009; Huang and Cao 2011). For engineering design (like larvae design) of flood mitigation structures, it is also essential to evalua
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