Daily river flow forecasting in a semi-arid region using twodatadriven

Rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resource management and there are many models in this field. among these models, the artificial neural network (ann) was found suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. in addition, anns are quick and flexible approaches which provide very promising results, and are cheaper and simpler to implement than their physically based models. therefore, this study evaluated the use of ann models to forecast daily flows in bar watershed, a semi-arid region in the northwest razavi khorasan province of iran. two different neural network models, the multilayer perceptron (mlp) and the radial basis neural network (rbf), were developed and their abilities to predict run off were compared for a period of fifty-five years from 1951 to 2006. the best performance was achieved based on statistical criteria such as rmse, re and sse. it was found that mlp showed a good generalization of the rainfall-runoff relationship and is better than rbf. in addition, 1-day antecedent runoff affected river flow, such that the statistical criteria decreased but the 5-day antecedent rainfall remained unaffected. furthermore, considering mlp, re and rmse, the best model produced the values 46.21 and 0.75 while the rbf model recorded 177.60 and 0.82, respectively.