An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management

Abstract

Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. The model is trained on data from Italian earthquakes recorded between 1972 and 2016, linking ground motion recordings to MCS observations located within 3 km. The dataset has been enhanced with site-specific correction factors to better capture local amplification effects. Key input features include Arias Intensity, spectral accelerations at four representative periods (0.15 s, 0.4 s, 0.6 s, and 2 s), and site condition proxies, such as slope and Vs30. The model achieves strong predictive performance (RMSE = 0.73, R2 = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. To ensure transparency, Shapley Additive Explanations (SHAPs) are used to quantify the contribution of each feature. Arias Intensity emerges as the dominant predictor, followed by spectral ordinates in line with structural response mechanics. As damage severity increases, feature importance shifts from PGA to PGV, while site-specific variables (slope, Vs30) act as refiners rather than amplifiers of shaking. The proposed approach enables near real-time prediction of local damage scenarios and supports data-driven decision-making in seismic emergency management.

Keywords

• macroseismic intensity
• ground motion parameters
• machine learning
• machine learning interpretability