Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event. (February 2023)

• Record Type: Journal Article
• Authors: Di Bacco, Mario; Rotello, Pierfrancesco; Suppasri, Anawat; Scorzini, Anna Rita
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Building damage -- Tsunami -- Machine-learning -- Feature importance -- Spatial transferability -- Japan; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105604
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study aims at developing an empirical, multi-variable tsunami damage model for buildings, based on machine-learning algorithms which leverage about 250.000 ex-post data surveyed by the Japanese Ministry of Land, Infrastructure and Transportation after the 2011 Great East Japan event in the Tōhoku region. By implementing simple geospatial tools, the dataset is integrated with additional explanatory variables, including, among others, factors accounting for the mutual interaction between the inundated structures. Tests on models' sensitivity to the number and type of input features used for model development reveal the importance, on the predictive performance, of considering usually neglected mechanisms like the shielding effect and the debris impact generation. The analysis for the potential spatial transferability indicates a reduction in the accuracy, thus suggesting a better suitability of empirical models for descriptive purposes, limiting their predictive ability only to region-specific cases. Highlights: Tsunami damage is the result of multiple hazard and vulnerability factors. Data driven approaches allow exploiting potentially explanatory information. Geospatial analysis can help in retrieving features to enhance models' accuracy. Machine learning models can provide useful information on feature importance. Spatial transferability can be a weak point for data driven models.