Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study

• DOI: 10.1016/j.heliyon.2024.e36606
• Publication Date: 2024-08-23T21:53:08Z
• Authors: Mohamed Gad; Aissam Gaagai; Asmaa A. Agrama; Walaa F.M. El-Fiqy; Mohamed Hamdy Eid; Péter Szűcs; Salah Elsayed; Osama Elsherbiny; Mosaad Khadr; Mostafa R. Abukhadra; Haifa E. Alfassam; Stefano Bellucci; Hekmat Ibrahim
• Keywords: Social sciences (General); H1-99; Q1-390; Science (General); Multivariate analysis; 0207 environmental engineering; 02 engineering and technology; Hydrogeochemistry; GIS; RNN; Human health risk; Research Article

Assessing and predicting quality of groundwater is crucial in managing availability effectively. In the current study, was thoroughly appraised using various indexing methods, including drinking water index (DWQI), pollution heavy metals (HPI), (PI), metal (MI), degree contamination (C