Wavelet ANN based monthly runoff forecast

摘要

A wavelet artificial neural network to forecasting monthly runoff is proposed.The monthly runoff series is firstly decomposed to sub-series on different time scales,and each sub-series is modeled.The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration.Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003).Finally,some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.