A Multivariate ANN-Wavelet Approach for Rainfall-Runoff Modeling

作者：Nourani Vahid^*; Komasi Mehdi; Mano Akira

来源：Water Resources Management, 2009, 23(14): 2877-2894.

DOI：10.1007/s11269-009-9414-5

• Artificial neural network
• Black box model
• Rainfall-runoff modeling
• Wavelet transform
• Ligvanchai watershed

Without a doubt the first step in any water resources management is the rainfall-runoff modeling over the 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-runoff modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall-runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were 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 runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.

• 出版日期2009-11