Rainfall Prediction Using Generalized Regression Neural Network: Case Study Zhengzhou

Although many models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. In this paper, we propose the application of generalized regression neural network (GRNN) model to predict annual rainfall in Zhengzhou. The results prove that GRNN has more advantage in fitting and prediction compared with back propagation (BP) neural network and stepwise regression analysis methods. The simulation results of GRNN for annual rainfall is better than that of BP neural network. And the accuracy of the prediction results is higher than that of BP neural network. The stepwise regression method is inferior to both of them in the accuracy of simulation and prediction results. In short, GRNN network structure is simple, calculate rapidly and stability. Compared with the traditional linear model and BP neural networks, the GRNN has smaller prediction error.