Back propagation neural network rainfall prediction model based on particle swarm optimization

It is a feasible method to use neural network construction to predict rainfall in the region. However, the error of this method is bigger, and its error is from the neural network structure itself. In view of the problem that the rainfall prediction precision based on BP neural network construction is low, this thesis proposes to optimize BP neural network rainfall prediction model with particle swarm optimization (PSO) algorithm, and then use the same training samples and testing samples to conduct a simulation experiment to the forecast model before and after optimization. The results show that the experimental testing result of the forecast model after optimization is more in line with the actual value.