Multi-source driven estimation of earthquake economic losses: A comprehensive and interpretable ensemble machine learning model

Abstract

Rapid and precise quantification of economic losses post-earthquake is critical for crafting informed disaster management strategies by governmental and insurance entities. This study introduces an ensemble model, constituted by Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost), tailored for quick and interpretative prediction of GDP-related seismic loss assessments, with Sichuan Province serving as the empirical backdrop. The ensemble approach, pre-trained for expediency, draws on a knowledge-driven selection of 30 features that span seismic risks, socio-economic variables, and exposure factors. Enhanced by feature scaling and Bayesian hyperparameter optimization, the model's efficacy is validated through 6 metrics such as R-Squared, MSE, and MAE etc. The incorporation of SHAP values further unravels the model's decision-making, providing transparency to the often opaque computations of machine learning. This methodology offers a scalable, interpretable framework that equips stakeholders with timely and accurate insights for risk mitigation, ultimately strengthening earthquake resilience.