Statistical and predictive analysis based on the factors affecting the severity of coal mine disasters.

Abstract

This study analyzes China's national coal mine accident data (2000-2025) to evaluate safety performance. Using one-way analysis of variance and random forest regression model, the study quantitatively analyzes temporal trends, spatial clustering, accident causes and types. A time-series model was developed to predict the million-ton mortality rate, and the optimal ETS(M,Ad,N) realizes a dynamic 5-year mortality rate forecast. Results show fatal accidents peak in March, June-August and November-December. Geographically, central and southwestern regions are high-risk, with Shanxi Province alone accounting for 15.6% of accidents. Analysis of accident causes revealed that 47.2% were attributable to human factors. By type, mechanical accidents were most common, and fire accidents were the least. Based on historical statistical trends, the ETS(M,Ad,N) model projects that the mortality rate per million tons will continue to decline from 2026 to 2030. These quantitative findings provide a data-driven reference for prioritizing safety interventions and targeted risk management strategies.