Artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFIS-SC) and support vector machine models were used to determine total dissolved solids (TDS) of the Zayendehrood River in Iran. In total, nine parameters (Ca2+, SO42-, Na+, Cl-, EC, pH, HCO3-, Mg2+ and SAR) were utilized to estimate the TDS of the river at a monthly time scale. Statistical data were categorized into low-flow and wet periods based on river discharge. Principal component analysis (PCA) was used in order to determine the input of the models. The results indicate that the PCA method, in both wet and low-flow periods, performed suitably based on the evaluation criteria for all models. The parameters of the first component included Ca2+, SO42-, Cl-, EC, Mg2+ and SAR in both periods. In contrast, the parameters pH and HCO3- of the second component provided unacceptable precision. The ANFIS-SC model was more precise than the other two models, with an RMSE value of 12.33 for the first component in the low-flow period. However, the ANN model was most precise in the wet period, with a calculated RMSE value of 13.87.
