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Being a good quality water resource, groundwater was over used during the last three decades to serve high water demand due to rapid growth in Bangkok and its vicinity. Excessive pumping rate of groundwater in Bangkok results in land subsidence problem and groundwater quality deterioration due to saltwater intrusion into shallow aquifers adjacent to the coast. This study applied a simple linear Genetic Algorithm (GA) model as an alternative tool for monitoring and forecasting of groundwater table. Nonthaburi aquifer, one of three major aquifers amongst seven aquifers in greater Bangkok area, was analyzed in the study. Monthly groundwater table of 43 monitoring wells, amongst 92 wells, 12 years (1997-2009) data was analyzed with land use map. GA was used to divide groundwater basin into sub-regions. Comparison between capability of GA and Artificial Neural Network (ANN) models for prediction of groundwater level reveals that ANN model has a better performance for all cases. However, GA model might be used to predict groundwater level with an acceptable accuracy (9% to 26% relative error). Better performance was obtained in medium to high residential area and industrial area (9-19% relative error). Due to its simplicity as well as period of record length of data requirement, GA is another appropriate alternative tool for monitoring and forecasting groundwater table fluctuation particularly for insufficient data area.