In recent decades, qualitative and quantitative assessments of groundwater sources reveal that efficient and accurate optimization approaches may assist in solving the multiple problems in evaluating groundwater quality. Hybrid models have been accepted and used in recent decades as a potentially useful approach for modeling water resource management processes in many different fields. Combined prediction models have more accurate outcomes than conventional methods. For this objective, three optimization meta-heuristic approaches, including Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Artificial Bee Colony (ABC), as well as intelligent models of Artificial Neural Networks (ANN), were employed to mimic groundwater quality. The input variables were Cl, SO42−, HCO3−, Na+, Mg2+, Ca2+, Na percent, K+, pH, and total hardness (TH) as one of the water’s necessary quality factors for drinking/irrigation was output. To reach this purpose, the data on groundwater quality for the Shiraz plain were employed for a period of 16 years (2002–2018). As a result, for the training RMSE and R2 databases, the estimated accuracy indices for the suggested hybrid HHO-ANN, GWO-ANN, and ABC-ANN models were (0.03907, 0.00427, 0.1078) and (0.99258, 0.99991, 0.94451), respectively, also for the testing RMSE and R2 databases, these models were determined to be (0.03592, 0.00365, and 0.11944) and (0.99416, 0.99995, and 0.92628), respectively, for the testing datasets. Finally, the outcomes illustrated the high accuracy and capability of the GWO-ANN approach in simulating and appraising the quality of groundwater.
