PSO_SVM prediction model for evaluating water inrush risk from deep coal seam floor

In order to better predict water inrush risk from deep coal seam floor，the mathematic model for failure depth predicted value of deep coal seam floor was obtained by using multiple linear regression theory．Based on this，the influences of confined aquifer pressure， thickness of aquifuge，length of panel and buried depth were considered as input vectors of the prediction model for evaluating deep water inrush risk． The prediction model for evaluating water inrush risk from deep coal seam was built，which optimal penalty factor and optimal kernel function parameter of SVM was obtained by PSO． The predictive accuracy of the model was compared with water inrush coefficient method，Mahalanobis distance discriminant method and Bayes discriminant method． The results show that the correlation coefficient be- tween fitting results calculated by mathematic model for failure depth of deep coal seam floor and measured results had a high fitting de- gree，which means the mathematic model had a higher accuracy． Taking the failure depth of deep coal seam floor into consideration，the model for water inrush risk from deep coal seam floor provided better results，which prediction accuracy was higher than the water inrush coefficient method，Mahalanobis distance discriminant method and Bayes discriminant method．