Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR)

Abstract

Least square support vector regression (LSSVR) is a powerful data-driven method for simulation and forecasting, with two parameters to tune. In this study, these parameters were automatically tuned using the interior search algorithm (ISA) and genetic algorithm (GA). The main purpose is in situ simulation and forecast of monthly groundwater level in Karaj plain, Iran, using historical groundwater level, precipitation, and evaporation data. The results of the interior search algorithm-least support vector regression (ISA-LSSVR) and genetic algorithm-least support vector regression (GA-LSSVR) compared with genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS). Based on average Nash-Sutcliffe criterion, the results revealed that the ISA-LSSVR improves the simulation and forecasting accuracy compared to other methods. Also, the results of the different model structure selection indicate that including precipitation and evaporation does not necessarily improve simulation and forecasting accuracy, but it would increase uncertainty. This increase suggests that groundwater level in the case study is affected by groundwater flow, recharge from leaky urban water infrastructure, and reduced recharge from precipitation due to impervious surfaces in urban areas rather than being solely governed by precipitation and evaporation. Finally, a sensitivity analysis was performed to assess the impacts of optimization algorithm parameters on the simulation and forecasting accuracy. The results indicate high and low sensitivity associated with GA and ISA, respectively. In conclusion, ISA-LSSVR was suggested as the best model due to computational efficiency, low sensitivity to its parameters, and high accuracy compared to other methods.