Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic non-carcinogenic health risks. In southeastern Nigeria, works used data-driven algorithms predicting PTEs groundwater are scarce. addition, only a few simulated water quality indices using machine learning modelling methods the region. Therefore, this study, physicochemical analyses were carried out on samples Nigeria. The laboratory results compute two indices: pollution index of (PIG) (WPI), ascertain quality. parameters served as input variables for multiple linear regression (MLR) artificial neural network (ANN) prediction Cr, Fe, Ni, NO3−, Pb, Zn, WPI, PIG. WPI PIG computation showed about 30–35% unsuitable human consumption, whereas 65–70% deemed suitable. insights from model also revealed lead (Pb) was most influential PTE degraded resources research area. findings MLR ANN models indicated strong positive accuracies (R2 = 0.856–1.000) with low modeling errors. predictive generally outperformed those PTEs. produced study predicted better compared previous studies. Thus, work provides into effective sustainability, management, protection.