Forecasting Daily Stream Flows of Vaniar River Using Genetic Programming and Neural Networks Approaches

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Forecasting Daily Stream Flows of Vaniar River Using Genetic Programming and Neural Networks Approaches

Original Article, C31
Roushangar K, Vojoudi Mehrabani F, Alami M.T
Journal. Civil Eng. Urban. 3(4):197-200. 2013

ABSTRACT:This study compares three different artificial intelligence approaches, namely, gene expression programming (GEP), and artificial neural networks (ANN), in daily as well as monthly streamflow forecasting. Daily streamflow data from Vaniar River in the Northwestern Iran were used. Coefficient of determination, root mean square error and scatter index were used to compare simulation results. The study demonstrates that the optimal results were obtained from the triple-input models including streamflows of current and two previous days and that the GEP model performed better than the ANN model in daily streamflow forecasting.
Keywords: Gene Expression Programming, Neural Networks, Forecasting

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