Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, two- and three-day ahead water table depths. The root mean square errors (RMSE), scatter index (SI), Variance account for (VAF) and coefficient of determination (R2) statistics are used for evaluating the accuracy of models. Based on the comparisons, it was found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. However, GP is superior to ANFIS in giving explicit expressions for the problem.