期刊名称：Eurasia Journal of Mathematics, Science & Technology Education
印刷版ISSN：1305-8223
电子版ISSN：1305-8223
出版年度：2018
卷号：14
期号：5
页码：1747-1757
DOI：10.29333/ejmste/85119
出版社：Pamukkale Univ Dept Sci Education
摘要：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.
