A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for prediction of Ligvanchai watershed precipitation at Tabriz, Iran. For this purpose. the main time series was decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the precipitation I month ahead. The obtained results show the proposed model can predict both short-and long-term precipitation events because of using multi-scale time series as the ANN input layer. (C) 2008 Elsevier Ltd. All rights reserved.