Predicting Seismic Intensity Using Machine Learning With Ancillary Data: Evidence From Destructive Earthquakes in China

Abstract

Accurate and rapid assessment of seismic intensity is crucial for postearthquake emergency response. This is especially true during the initial “black‐box” period, when real‐time data are scarce. To address this challenge, we developed a machine learning‐based (ML) framework for predicting kilometer‐grid‐scale seismic intensity distribution. We selected 50 destructive earthquakes that struck western China after 2003 as case studies and then constructed a high‐dimensional covariate dataset for them. The dataset integrates 39 predictors tied to mainshock intensity, covering seismic parameters, socioenvironmental indicators, and natural geographic attributes. Feature importance ranking was employed to select the top predictive covariates, which were then combined with digitized observed intensity values to train four ML models. The optimized Random Forest model achieved the best prediction performance. Its overall accuracy ranged from 85.3% to 98.7% on independent validation earthquake cases. The corresponding RMSE was 0.21–0.39, and the R2 reached 0.88–0.97. Evaluations based on independent earthquake cases indicate that the predictions can reasonably capture the location, extent, and severity of heavily damaged zones. This approach offers a promising, data‐adaptive supplementary tool for rapid postearthquake damage assessment, particularly in regions with limited seismic monitoring infrastructure.