Thunderstorm disaster prediction based on enhanced GWO optimized BP neural network

Abstract

Aiming at the problem that traditional BP algorithm is prone to fall into local optimum when carrying out thunderstorm disaster prediction, a thunderstorm disaster prediction method based on BP neural network optimized by enhanced GWO algorithm is proposed in this paper. Firstly, the global search capability of GWO is enhanced by introducing clone mutation operation in genetic algorithm and position update idea in particle swarm optimization, and the convergence speed of the algorithm is improved. Then, the BP neural network weight and threshold optimized by the enhanced GWO algorithm are used to build the network model and predict the occurrence of thunderstorm disasters. Simulation results show that compared with the original BP algorithm, GWO-BP algorithm and PSO-BP algorithm, the improved IPSGWO-BP algorithm improves the accuracy of thunderstorm disaster prediction by 13.33%, 12.50% and 8.33%, respectively. Meanwhile, the null alarm rate is lower, the optimization ability is stronger, and the convergence speed is faster.