Prediction model of spontaneous combustion risk of extraction borehole based on PSO-BPNN and its application.

The feasibility and accuracy of the risk prediction of gas extraction borehole spontaneous combustion is improved to avoid the occurrence of spontaneous combustion in the gas extraction borehole. A gas extraction borehole spontaneous combustion risk prediction model (PSO-BPNN model) coupling the PSO algorithm with BP neural network is established through improving the connection weight and threshold values of BP neural network by the particle swarm optimization (PSO) algorithm. The prediction results of the PSO-BPNN model are compared and analyzed with that of the BP neural network model (BPNN model), GA-BPNN model, SSA-BPNN model and MPA-BPNN model. The results showed as follows: the average relative error of the PSO-BPNN model was 4.38%; the average absolute error was 0.0678; the root mean square error was 0.0934; and the determination coefficient was 0.9874. Compared with the BPNN model, the average relative error, average absolute error and root mean square error decreased by 9.35%, 0.1707 and 0.2056 respectively; and the determination coefficient increased by 0.1169. Compared with the GA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.19%, 0.0602 and 0.0821 respectively; and the determination coefficient increased by 0.0320. Compared with the SSA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 5.70%, 0.0820 and 0.1100 respectively; and the determination coefficient increased by 0.0474. Compared with the MPA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.50%, 0.0861 and 0.1125 respectively; and the determination coefficient increased by 0.0488, proving that the PSO-BPNN model is more accurate than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model as for prediction. When the PSO-BPNN model was applied to three extraction boreholes A, B, and C in a coal mine of Shanxi, the prediction results were better than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model, proving the accuracy and stability of the PSO-BPNN model in predicting risk of borehole spontaneous combustion in other mine.