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Correct simulation of groundwater level fluctuations is very important for optimum water resources management as it might affect the (subsurface) irrigation scheduling, groundwater exploitation studies, surface/groundwater interactions and identifying the aquifer characteristics. Such variations in coastal aquifers are more complicated due to the simultaneous effects of tide variations, especially in small time steps (e.g. hourly scales). The present study evaluated the capabilities of different heuristic data driven models for simulating groundwater table depth (GWD) fluctuations (with different lag times) in a coastal aquifer using the GWD, rainfall, and tide records. A k-fold testing cross-validation data assignment method was adopted here for defining the training and testing blocks. The obtained results showed that introducing rainfall and tide records along with the GWD data as models’ inputs improved the models’ performance accuracy, although accurate models could be obtained using only tide and rainfall data as external input parameters of models. Therefore, considering the overall performance of the models (which produced lower error values for all considered input combinations), all the applied models could simulate the GWD variations using external tide and rainfall data (without using GWD records).

Prediction of groundwater level variations in coastal aquifers with tide and rainfall effects using heuristic data driven models