To achieve accurate warning of spontaneous combustion fire in goaf and avoid it,a coal spontaneous combustion prediction model combined with dung beetle optimizer(DBO)and support vector machine(SVM)algorithm is introduced.Ten indexes such as O2 and N2 are selected as the input indexes of spontaneous combustion prediction,and the spontaneous combustion risk level is used as the output index to train the established model.Four classification performance evaluation indexes are used to test the prediction performance and accuracy of the model.Meanwhile,the pre-diction results of DBO-SVM model,back propagation neural network(BPNN)model optimized by DBO,BPNN neural network model optimized by particle swarm optimization algorithm(PSO)and SVM model are compared and analyzed.The results show that the accuracy of DBO-SVM model is increased by 13.33％,20％and 33.33％respectively compared with DBO-BPNN、PSO-BPNN and SVM models.Finally,the model is applied to the prediction of coal spontaneous combustion in the working face of Jinniu Coal Mine in Shanxi Province.The prediction results show that the DBO-SVM model can quickly and accurately predict the risk of spontaneous combustion fire in goaf of different mines,indicating that the DBO-SVM model is more universal and stable than other mod-els,and that it is more suitable for the prediction of spontaneous combustion of boreholes.
万方数据
维普
