Comparison of Neuro-fuzzy and SCS methods in sub-watersheds prioritization for watershed measures (Case study: Taleghan watershed)

Because of insufficient factors including facilities، budget، human resources as well as time watershed operation is not applicable throughout the basin. As a result، watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as destruction، degradation; physical and financial damage and also flooding are considerably high. In addition، due to hydrometric stations، defects or the lack of stations in some areas، some efforts have been made experts recently to assess and consequently introduce some novel and reliable methods for prioritizing on the basis of current data obtained from sub-basins features of different geographical regions. In current study، the utilization possibilities of neuro-fuzzy technique and SCS in HEC-HMS model that have different potential to examine a wide range of advantageous and disadvantageous in making various decisions were studied. To determine the prediction accuracy of these methods، the rate of flow and sediment output of Taleghan sub-basins were taken over one year. The results of these methods were then compared with the maximum two-year return period flow observations. Our results revealed that in making prioritization، neuro-fuzzy as compared with the SCS method can produce the best prediction as long as the coefficients of errors، efficiency compared to the observational data and predictions are taken into account.