Advanced Mathematical Modeling for Predictive Analytics of Rock Burst Hazards in Deep Coal Mining Excavations

This study addresses the challenging issue of predicting rockburst disasters during deep coal mining and proposes a monitoring process modeling scheme that integrates Kalman filtering, wavelet analysis, random forest, SVM, and ARIMA model. Kalman filtering is utilized to optimize data preprocessing, achieving data error correction and mean normalization, which provides a solid foundation for data analysis. Wavelet analysis in conjunction with random forest is employed to accurately identify interference components in electromagnetic radiation and acoustic emission signals, with an accuracy rate of 96% and 97%, respectively. The introduction of signal growth rate features and the use of SVM to construct a binary classification model, enhanced by a random sampling strategy, improves the model's generalization capabilities. For the prediction of non-continuous time series, linear interpolation and ARIMA model are adopted, combined with wavelet feature extraction, effectively estimating the probability of precursor signal occurrence. The proposed scheme demonstrates high accuracy, strong robustness, and excellent predictive capabilities, providing a scientific basis for coal mine safety production and pointing the way for further model optimization and prediction accuracy improvement.