A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall
Shiliang Zhang 1 * , Tingcheng Chang 1, Dejing Lin 2
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1 School of Information and Electrical Engineering, Ningde Normal University, Jiaocheng District, Ningde, P.R. CHINA2 United Network Communications Co., Ltd., Fuzhou, P.R. CHINA* Corresponding Author


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.


This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2018, Volume 14, Issue 5, 1747-1757


Publication date: 07 Feb 2018

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