A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall

Abstract

In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting rainfall with the data of antecedent monthly rainfalls, the ant colony optimization algorithm (ACO) is combined with particle swarm optimization algorithm (PSO) to improve performance of artificial neural network (ANN) model. ACO is adopted to initialize the network connection the weights of and thresholds of WNN and PSO is used to update the parameters of ACO, HWNN can avoid falling into a local optimal solution and improve its convergence rate and obtain more accurate results. In simulations based on monthly rainfall data from the city of Ningde in the southeastern China. The forecasting performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error, root mean square error and average absolute percentage error. The results show that the HWNN model improves the monthly rainfall forecasting accuracy over Ningde in comparison to the reference models. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.