Prediction model of water inrush in coal mine based on IWOA-SVM

The traditional prediction algorithm of water inrush in coal mine is easy to fall into local optimum, the prediction results accuracy is low and the speed is slow. In order to solve the above problems, a prediction model of water inrush in coal mine based on improved whale optimization algorithm (IWOA) and support vector machine (SVM) is proposed. IWOA improves the whale optimization algorithm (WOA) from three aspects, whale population initialization, nonlinear adjustment factor and random differential evolution (DE). Tent mapping is used to initialize the whale population to improve the possibility of the whale population finding the optimal prey. The non-linear change strategy of the adjustment factor is applied to improve the global search capability of the algorithm in the early stage of the iteration and the local search capability in the later stage of the iteration so as to speed up the convergence speed. The mutation, crossover and selection operations of DE algorithm are introduced to enhance the global search capability of WOA. The parameters of SVM model are optimized by IWOA. The six factors affecting water inrush in coal mine, including water pressure, thickness of aquiclude, dip angle of coal seam, fault drop, distance between fault and working face and mining height are taken as the input characteristic vectors of the model. The two water inrush results of water inrush and safety are taken as the output vectors. The objective function is established to minimize the error between the water inrush prediction results and the actual results, and the coal mine water inrush prediction model based on IWOA−SVM is obtained. The experimental results show that IWOA has the highest prediction accuracy, minimum standard error, fast convergence and good robustness compared with particle swarm optimization, DE algorithm and WOA. The accuracy of water inrush prediction of IWOA−SVM is 100%. Compared with the traditional water inrush coefficient method, SVM and WOA−SVM, IWOA−SVM shows higher accuracy and stability.