A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida

Accurate and reliable prediction of groundwater level is essential for water resource development and management. This study was carried out to test the validity of three nonlinear time-series intelligence models, artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference system (ANFIS) in the prediction of the groundwater level when taking the interaction between surface water and groundwater into consideration. These three models were developed and applied for two wells near Lake Okeechobee in Florida, United States. 10 years data-sets including hydrological parameters such as precipitation (P), temperature (T), past groundwater level (G) and lake level (L) were used as input data to forecast groundwater level. Five quantitative standard statistical performance evaluation measures, correlation coefficient (R), normalized mean square error (NMSE), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and Akaike information criteria (AIC), were employed to evaluate the performances of these models. The conclusions achieved from this research would be beneficial to the water resources management, it proved the necessity and effect of considering the surface water-groundwater interaction in the prediction of groundwater level. These three models were proved applicable to the prediction of groundwater level one, two and three months ahead for the area that is close to the surface water, for example, the lake area. The models using P, T, G and L achieved better prediction result than that using P, T and G only. At the same time, results from ANFIS and SVM models were more accurate than that from ANN model.