A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

The recent losses caused by the unprecedented 2011 Great East Japan Tsunami disaster have stimulated further research efforts, notably in the mechanisms and probabilistic determination of tsunami-induced damage, in order to provide the necessary information for future risk assessment and mitigation. The stochastic approach typically adopts fragility functions, which express the probability that a building will reach or exceed a predefined damage level usually for one, sometimes several measures of tsunami intensity. However, improvements in the derivation of fragility functions are still needed in order to yield reliable predictions of tsunami damage to buildings. In particular, extensive disaggregated databases, as well as measures of tsunami intensity beyond the commonly used tsunami flow depth should be used to potentially capture variations in the data which have not been explained by previous models. This study proposes to derive fragility functions with additional intensity measures for the city of Kesennuma, which was extensively damaged during the 2011 tsunami and for which a large and disaggregated dataset of building damage is available. In addition to the surveyed tsunami flow depth, the numerically estimated flow velocities as well as a binary indicator of debris impact are included in the model and used simultaneously to estimate building damage probabilities. Following the recently proposed methodology for fragility estimation based on generalized linear models, which overcomes the shortcomings of classic linear regression in fragility analyses, ordinal regression is applied and the reliability of the model estimates is assessed using a proposed penalized accuracy measure, more suitable than the traditional classification error rate for ordinal models. In order to assess the predictive power of the model, penalized accuracy is estimated through a repeated tenfold cross-validation scheme. For the first time, multivariate tsunami fragility functions are derived and represented in the form of fragility surfaces. The results show that the model is able to predict tsunami damage with satisfactory predictive accuracy and that debris impact is a crucial factor in the determination of building collapse probabilities. Copyright The Author(s) 2015