Spatiotemporal and hydrogeological assessment of groundwater supported by soft computing modeling of heavy metal in Al-Hassa, Eastern Province, Saudi Arabia

As one of the vulnerable arid regions, Saudi Arabia suffered from an optimal water crisis. Rapid population, industrialization, and urbanization mandated the water stress and agricultural water balance in the region; as such, there is a need for integrated water resources planning and management. To meet sustainable development goal six (SDGs-6), the ground and surface water need to be within the required quality and quantity. Hence there is a need for both the physical, chemical and hydrogeological assessment of groundwater in the Eastern part of Saudi Arabia. This study proposed three scenarios to assess the hydrogeological groundwater quality, namely, experimental laboratory based on fieldwork, geospatial mapping of the parameters (EC, pH, CaCO₃, Turbidity, NA, k, Mg, Ca, F, Cl, Br, Li, B, Al, V, Cr, Fe, Mn, Ni, Co, Cu, Zn, As, Se, Sr, Mo, and Ba), and intelligent computational analysis using machine learning (ML). This research is motivated toward intelligent prediction of some heavy metals using neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS). The evaluation criteria of the predictive results were analyzed based on mean absolute percentage error (MAPE), correlation coefficient (CC), and determination coefficient (DC). The outcomes proved the satisfactory ability of the NFIS model over the NN approach, despite its predictive credit.