Inverse groundwater salinization modeling in a sandstone's aquifer using stand-alone models with an improved non-linear ensemble machine learning technique

Groundwater (GW) salinization is among several hydro-environmental processes that essentially influence water resources management, freshwater supply, water scarcity and sustainability. Machine Learning (ML) is an effective and robust tool for GW monitoring, primarily when chaotic and interrelated parameters influence the system. The current study presents the first inverse modelling of GW salinization in sandstone aquifer using stand-alone (Elman NN, GRNN, BPNN, and ILR) models coupled with nonlinear ensemble ML (GPR-E, SVR-E, and ANFIS-E) techniques. For this purpose, 25 input parameters were imposed on data mining; subsequently, inversely correlated variables including bicarbonate (HCO₃⁻), nitrate (NO₃⁻), Temperature (Temp), Lithium (Li), and Manganese (Mn) were used for modelling the Electrical conductivity (EC). Neuro-sensitivity feature selection (NSFS) ranking was used to determine the input combinations. The Modeling schema was based on the actual field and experimental data. The results indicated the potential of GRNN-C1 with a value of NSE = 0.998, and 0.928 in both calibration and validation phases, respectively. The outcomes revealed weaknesses in all other stand-alone models with low efficiency ranged from 0 to 60%. The attained ensemble results proved promising for all, with ANFIS-E-C1 (RMSE = 2.993, MAE = 0.879, and NSE = 0.999) emerging best in the validation phase. The overall proposed ensemble learning proved the excellent capability of ML to handle complex GW processes and served as a reliable predictive tool for water resources management. The contribution-based methodologies explored in this study involved inverse modelling, nonlinear feature selection, and nonlinear ensemble techniques, which was the first study in GW salinization.