Author(s): Jose P. Matos; Theodora Cohen Liechti; Maria M. Portela; Anton J. Schleiss
Linked Author(s): Anton J. Schleiss
Keywords: Rtificial Neural Networks; Flow forecast; Kariba; Support-Vector Regression; Zambezi
Abstract: Accurate river flow forecasting is an important asset for stream and reservoir management, being often translated into substantial social, economic and ecological gains. This contribution aims at coupling satellite rainfall estimates and machine learning techniques for daily flow forecast. Two lead times, of 30 and 60 days, were tested for flows at Victoria Falls, in Southern Africa. Six distinct machine learning models were compared with optimized ARMA models and benchmarked against a Fourierseries approximation. Results show that the addition of rainfall data generally enhanced the performances of machine learning models at 30 days but did not improve forecasts at 60 days. Also, it was shown that traditional ARMA models do not make use of the rainfall information. Regarding a lead time of 60 days, the machine learning models appear to bear great advantages compared to ARMA models which, for such a lead time have shown practically no forecast capabilities.
Year: 2013