Prediction of height of water flowing fractured zone based on improved SSA to optimize BP neural network

Abstract

Aiming at the problem that it is difficult to accurately predict the height of water flowing fractured zone in coal mining subsidence disaster early warning modeling, a prediction model based on improved sparrow search algorithm (SSA) to optimize BP neural network is proposed to predict the height of water flowing fractured zone. Add Tent chaotic mapping to the standard SSA to initialize the population, improve the uniformity and diversity of population distribution, and enhance the global search ability of SSA; with the help of Gaussian mutation and Gaussian perturbation algorithm, the ability of SSA to escape from local optimization is improved; dynamic step size is introduced to instead of random step size to improve the solution accuracy of SSA. Through practical application, the improved SSA-BP was compared with SSA-BP, PSO-BP, BP and the prediction results of GA-SVR model studied by predecessors. The results show that the average absolute error (MAE), average absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient(R2) of the improved SSA-BP prediction model are 1.23 m, 2.64%, 1.51 m and 0.985 respectively. It is better than other models and improves the accuracy of predicting the height of water flowing fractured zone.

Keywords

• water flowing fractured zone
• bp neural network
• sparrow search algorithm(ssa)
• tent chaotic mapping
• gaussian mutation