Annual precipitation forecast of Guangzhou based on genetic algorithm and backpropagation neural network (GA-BP)

Abstract

Considering that the traditional BP neural network model has the disadvantages of easily falling into local optimum, slow convergence and sensitive to change of initial input, a method combining the traditional BP neural network and genetic algorithm (GA) is introduced. Considering the disadvantage of the traditional BP neural network models such as local optimum, slow convergence and sensitive to the change of initial input， the author introduces a method combining the traditional BP neural network and genetic algorithm in this paper. When the global optimization is achieved by a genetic algorithm, the optimized weight matrix is substituted into the training network, which is used as the initial input of the BP neural network for training. The total annual precipitation from 1951 to 2019 of a meteorological station in Guangzhou, is selected as a studying example to verify the model’s effectiveness. The results show that the genetic algorithm (GA) - BP neural network method can effectively improve the prediction accuracy and enhance the prediction capability for the extreme precipitation values. Thus, the genetic algorithm (GA) - BP neural network method is more suitable for precipitation prediction in Guangzhou than the traditional BP model and has a positive effect on environment protection.