Comprehensive approach integrating remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems.

ABSTRACT: The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all water resources in Siwa Oasis and analyzed for physical (pH, T°C, EC, and TDS) chemical (SO₄ ^2-, HCO₃ ^-, NO₃ ^-, Cl^-, CO₃ ^2-, SiO₂, Mg²⁺, Na⁺, Ca²⁺, and K⁺), and trace metals (AL, Fe, Sr, Ba, B, and Mn). A digital elevation model supported by machine learning was used to predict the change in the land cover (surface lake area, soil salinity, and water logging) and its effect on water quality d

SUBMITTER: Eid MH 

PROVIDER: S-EPMC11252974 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature