The accurate recognition of hydrogeological parameters such as transmissivity, hydraulic conductivity and storage coefficient or specific yield are the most important parameters for predicting the aquifer conditions that are determined at different points of aquifer with great cost. In recent years, artificial intelligence models have been used as alternatives method to adaptive graphical methods to determine the hydrodynamic parameters of aquifers. Therefore, in this study the Sugeno fuzzy logic was used to determine the hydrodynamic parameters of the confined aquifer. First, the accuracy, reliability and generalization ability of the fuzzy model is verified by time-drawdown field data. Then, the results of this model were compared with the results of obtained from the Theis graphical method and artificial neural network. Comparison of the RRMSE of the Sugeno fuzzy model and the artificial neural network for determining the transmissivity and storage coefficient in testing step show that the fuzzy model reduces the error relative to the neural network 9.21% and 11.66%, respectively. Therefore, the results in the verification step indicate that the Sugeno fuzzy model is more accurate than the Theis graphical method and artificial neural network due to its high ability in handling uncertain data.
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