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Accurate prediction of groundwater table is important for the efficientmanagement of groundwater resources. Despite being the most widely usedtools for depicting the hydrological regime, numerical models suffer fromformidable constraints, such as extensive data demanding, high computationalcost, and inevitable parameter uncertainty. Artificial neural networks(ANNs), in contrast, can make predictions on the basis of more easilyaccessible variables, rather than requiring explicit characterization of thephysical systems and prior knowledge of the physical parameters. This studyapplies ANN to predict the groundwater table in a freshwater swamp forest ofSingapore. The inputs to the network are solely the surrounding reservoirlevels and rainfall. The results reveal that ANN is able to produce an accurateforecast with a leading time of 1 day, whereas the performance decreaseswhen leading time increases to 3 and 7 days

Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest