De Risi Raffaele, Goda Katsuichiro, Mori Nobuhito, Yasuda Tomohiro
1Department of Civil Engineering, Queen's Building University Walk, University of Bristol, Bristol, BS8 1TR UK.
2Disaster Prevention Research Institute, Kyoto University, Kyoto, 611-0011 Japan.
Stoch Environ Res Risk Assess. 2017;31(5):1253-1269. doi: 10.1007/s00477-016-1230-x. Epub 2016 Feb 18.
Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.
经验性海啸易损性曲线是基于贝叶斯框架开发的，通过系统且全面地考虑输入海啸灾害数据的不确定性。研究考虑了三种易损性建模方法，即对数正态方法、二项逻辑回归方法和多项逻辑回归方法，并将其应用于2011年东北地震的大量海啸破坏数据。本研究的一个独特之处在于，通过比较两个海啸淹没/浪高数据集（一个由日本国土交通省提供，另一个由东北海啸联合调查组提供）来量化海啸淹没数据的不确定性（即易损性建模中的输入灾害数据），然后通过贝叶斯统计方法进行传播，以评估其对海啸易损性模型的影响。数据和方法的系统实施有助于在不同假设下对海啸易损性模型进行定量比较。这种比较表明，在所考虑的模型中，使用未分组数据的二项逻辑回归方法更为可取；然而，仍需要对使用未分组数据的多项逻辑回归进行进一步研究。最后，将所开发的海啸易损性函数与建筑物破坏损失模型相结合，以研究不同海啸易损性曲线对海啸损失估计的影响。数值结果表明，输入海啸数据的不确定性不可忽略（变异系数为0.25），忽略输入数据的不确定性会导致对模型不确定性的高估。
