Application of Artificial Intelligence Method Coupled with Discrete Wavelet Transform Method

In modern hydrology one of the most important applications is hydrological time series forecasting, particularly for effective information related to reservoir system. In this study, artificial neural network (ANN) such as radial basis function neural network (RBFNN), coupled with time series decomposing method (TSDM), named as discrete wavelet transform (DWT) to forecast monthly time series at upper Yangtze River and Xianjiababa is taken as the forecast hydrological station. Data has been analyzed by comparing the simulation outputs delivered by models with two performance indices named as (a) correlation coefficient and root mean square errors, which can be denoted by (R) and (RMSE) respectively. Results show that time series decomposition technique discrete wavelet transform method have shown more accuracy and can play important role to improve the corrected in discharge prediction, as compared to single ANN's.