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Data-Driven Earthquake Multi-impact Modeling:A Comparison of Models

International Journal of disaster Risk Science 2024年第3期15卷 421-433页

作者： Hamish Patten Max Anderson Loake David Steinsaltz Information Management International Federation of Red Cross and Red Crescent Societies(IFRC)1211GenevaSwitzerland Department of statistics University of OxfordOxfordOX13LBUK

In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,*** the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 *** the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 *** k-fold,3-repeat cross validation was used to ensure out-of-sample predictive *** importance of several variables used as proxies for vulnerability to disasters indicates covariate *** 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme ***,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of ***,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.

关键词： disaster risk modeling Earthquake impact models Machine learning disaster statistics Satellite image-derived buildingdamage

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