Forecasting daily stream flows using artificial intelligence approaches
2012
Shiri, Jalal | Kişi, Özgur | Makarynskyy, Oleg | Shiri, Abbas-Ali | Nikoofar, Bagher
This study compares three different artificial intelligence approaches, namely, gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), in daily stream flow forecasting of Alavian Dam Station on the Soofi-Cahi River in the Northwestern Iran. The study demonstrates that the optimal results were obtained from the triple-input models, including stream flows of current and 2 previous days, and that the GEP model performed better than the ANN and ANFIS models in daily stream flow forecasting. It was found that the optimal GEP model with coefficient of determination R ² = 0.924, root mean square error = 0.908 m ³/s and scatter index = 0.354 in the test stage was superior in forecasting daily stream flow to the optimal ANN and ANFIS models.
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