The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

Response time, describing how quickly a disturbed system would reach a new equilibrium, has been helpful to hydrogeologists in characterizing and understanding the hydrogeological systems. This study examined the complex response times associated with lake–groundwater perturbed by climate. Simulated hydraulic heads and lake stage values derived from a 3-D, MODFLOW-based model were used to calculate the response times for a closed, groundwater-fed lake system. Although obviously coupled, the response times of the lake and groundwater systems were different from one another. Typically, the adjustments in hydraulic heads occurred more rapidly than lake stage. Response times for groundwaters close to the lake were controlled by the lake because of the slow transient response in stage. However, the influence of the lake declined toward the basin boundaries. This behavior occurred because critical parameters controlling the response-time behavior of the groundwater system (e.g., recharge rate) differed from those controlling the response-time behavior of the lake (e.g., bed leakance). An improved understanding of lake–groundwater behaviors have the potential to evaluate how lakes function as systems for recording paleoclimates.