In order to improve the accuracy and efficiency of coal and gas outburst prediction,a coal and gas outburst prediction model based on data preprocessing multi-strategy improved fireworks algorithm(IFWA)optimized extreme learning machine(ELM)was proposed.Firstly,for the nonlinear multi-dimensional feature data,the grey relational analysis(GRA)was used for feature selection,the principal component analysis(PCA)was used for feature reduction,and the data index after data preprocessing was used as the input of the model.Secondly,the gravitational search operator and hybrid mutation strategy were introduced to improve the problem that the fireworks algorithm(FWA)was easy to fall into local optimum.Finally,IFWA was used to optimize the weight and deviation from the input layer to the hidden layer of ELM,and the optimal coal and gas outburst risk prediction model was constructed.The results show that the RMSE and R2 of IFWA-ELM model can reach 0.074 and 0.968,which are improved compared with ELM,GA-ELM,PSO-ELM and FWA-ELM models.The accuracy of prediction of IFWA-ELM model on coal and gas outburst risk level can reach 100％,which has better convergence speed and prediction accuracy.The research results can provide a reliable theoretical basis for multi-data fusion prediction of coal mine gas outburst.
Coal and gas outburstFireworks algorithmExtreme learning machineData preprocessingRisk prediction model
维普
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