Prediction of chlorine concentration, using ANFIS Model (Case Study: hydrometric station of Chamriz)

In recent decades forecasts for the management of surface water resources, especially in hot and dry areas of southern Iran which are suffering from lack of rainfall, are of particular importance. For this reason, we consider the discharge and quality parameters of rivers, which are the main water supply behind dams, and are among the most important factors in predicting surface water. In this study, fuzzy neural network model was used to predict the qualitative parameter of chlorine of hydrometric station of Chamriz (located on the River Kor). Overall, it was shown that the results of ANFIS model to estimate monthly average concentration of chlorine is acceptable. In addition, modeling results during the training phase are more appropriate than phase of testing. Finally, it was shown in ANFIS model, that the use of trapezoidal membership function leads to better results than the triangular membership function

Keywords:

average monthly concentration of chlorine , Hydrometric station of Chamriz , Kor river , ANFIS model and discharge