Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level

Groundwater level time series is a prime factor for variety of groundwater studies and is of great significance for the management of groundwater resources. Quality control of groundwater level observations is essential for hydrological applications. Artificial Intelligent techniques deal with highly nonlinear interactions and complex hydrological process and hence can be a better alternative for groundwater level prediction. In this research, the performances of Support Vector Regression (SVR) and SVR ensembled with metaheuristic Algorithm of Innovative Gunner (AIG) models were evaluated in simulating the monthly groundwater level of the Shabestar plain during the period 2001–2019. The 80 and 20% of the monthly dataset were used for training and testing the developed models. The efficiency of the developed models was compared using different statistical indices including correlation coefficient ( R ), Nash–Sutcliffe Efficiency (NSE) coefficient, Root-Mean-Square Error (RMSE), RMSE-observation standard deviation ratio (RSR) and Legates & McCabe’s Index ( E LM ). The results showed that the hybrid model (SVR-AIG) generates accurate estimations in combinatory patterns. Moreover, among the SVR and SVR-AIG models with different input scenarios, the SVR-AIG model showed best results for scenario 6 (M6) in both the training stage ( R = 0.995, NSE = 0.99, RMSE = 0.151 (m), RSR = 0.096 and E LM = 0.916) and the testing stage ( R = 0.941, NSE = 0.879, RMSE = 0.146 (m), RSR = 0.346 and E LM = 0.660). The hybrid SVR-AIG model is shown to be more accurate and robust than the SVR models, providing a novel capability to capture unknown time-varying dependencies. In general, the results of the proposed model are promising and it provides a reliable insight for water resources planners in conducting future research of groundwater resources. Graphical abstract