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Aquifer parameters are the important factors for assessing groundwater potential in any area. Yet estimation of aquifer parameters is expensive and time-consuming. This study proposes an optimal and improved model to make a quantitative and qualitative correlation between pumping test data set and aquifer parameters by integration of artificial neural network training algorithms and the supervised committee machine concept. This supervised committee machine with training algorithms (SCMTA) combines Levenberg–Marquardt (LM), Bayesian regularization (BR), gradient descent (GD), one-step secant (OSS) and resilient back-propagation (RP) algorithms using a supervised combiner to estimate non-leaky confined aquifer parameters using pumping test data set. Each of these algorithms has a weight factor showing its contribution in overall prediction. The results reveal that RP, BR and LM have more …