ComparingArtificial Neural Networks, Adaptive Neuro-Fuzzy and Salinity Rating Curve Models for Simulation of groundwater salinity (Case Study: coastal area Dam Hajylr)

due to the complexity of hydrological processes characteristic of the underground water table and their variability and the effect of different parameters on thPredict the salinity of groundwater is difficult. The use of artificial intelligence systems has expanded as a new tool in the analysis of water issues، research now were compared models in Neuro-Fuzzy Inference System (ANFIS)، Artificial Neural Networks (ANNs) and Rating Curve for measures Groundwater salinity For two seasons in a coastal area Dam Hajylr in East Azarbaijan province And using the data flow، salinity and water table level، the models and Rating Curve prepared. Also Courses Statistical into two seasons of low and probe water were divided. The results of this study showed that Neuro-Fuzzy Inference System Compared with Artificial Neural Networks and Rating curve Is more accurately and Between different models Neuro-Fuzzy Inference The first pattern with RMSE 1099/55 and R2/985 As the best model and Between different models Artificial Neural Networks The third pattern with RMSE 4066/87 and R2/82 was identified as the weakest Pattern. Superior of Neuro-Fuzzy Inference Modeling salinity peak And estimate annual salinity is Clearly than the other two models Especially Rating Curve