Groundwater always has been considered as one of the major sources of drinking and agricultural water supply, especially in arid and semi-arid zones. Thus, there is a need to simulate (i.e., forecast) groundwater levels with an acceptable accuracy. In this paper, we present two applications of intelligent optimization algorithms for simulations of monthly groundwater levels in an unconfined coastal aquifer sited in the Shabestar plain, Iran. First, the backpropagation neural network (ANN-BP) with seven neurons in its hidden layer is utilized to reproduce groundwater-level variations using the external input variables including the following: rainfall, average discharge, temperature, evaporation, and annual time series. In the next application, ant colony optimization is used to optimize and find initial connection weights and biases of a BP algorithm during the training phase (ACOR-BP). The results were found to be acceptable in terms of accuracy and demonstrated that a hybrid ACOR-BP model is a much more rigorous fitting prediction tool for groundwater-level forecasting. This study has shown that such a hybrid network can be used as viable alternative to physical-based models for simulating the reactions of the aquifer under conceivable future scenarios. In addition, it may be useful for reconstructing long periods of missing historical observations of the influencing variables.

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