Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques

The knowledge of groundwater table fluctuations is important in agricultural lands as well as in the studies related to groundwater utilization and management levels. This paper investigates the abilities of Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques for groundwater level forecasting in following day up to 7-day prediction intervals. Several input combinations comprising water table level, rainfall and evapotranspiration values from Hongcheon Well station (South Korea), covering a period of eight years (2001–2008) were used to develop and test the applied models. The data from the first six years were used for developing (training) the applied models and the last two years data were reserved for testing. A comparison was also made between the forecasts provided by these models and the Auto-Regressive Moving Average (ARMA) technique. Based on the comparisons, it was found that the GEP models could be employed successfully in forecasting water table level fluctuations up to 7 days beyond data records.