Research on coal and gas outburst risk prediction based on improved search algorithm optimized deep learning network

With the gradual deepening of coal mining, the surrounding rock pressure significantly increases, and the risk of gas release and accumulation also increases, increasing the likelihood of coal and gas outburst hazards. This study used boxplot and data interpolation method to preprocess data and used correlation to screen out highly correlated influencing factors as disaster prediction indicators. Build an initial prediction model framework using Convolutional Neural Network (CNN), optimize model hyperparameters using Chaos Mapping and Levy Flight Improved Crow Search Algorithm (ICSA), and establish a coal and gas outburst prediction model based on ICSA-CNN. Finally, a comparative model was established to compare the evaluation indicators and confusion matrix. According to the results, the ICSA-CNN model stood out as the most accurate in its predictive capabilities, better robustness and generalization ability, and higher security.