Heliyon, Год журнала: 2024, Номер 10(12), С. e32992 - e32992
Опубликована: Июнь 1, 2024
The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all resources Siwa Oasis analyzed for physical (pH, T°C, EC, TDS) chemical (SO42−, HCO3−, NO3−, Cl−, CO32−, SiO2, Mg2+, Na+, Ca2+, K+), trace metals (AL, Fe, Sr, Ba, B, Mn). A digital elevation model supported by learning was used predict the change land cover (surface lake area, soil salinity, logging) its effect on deterioration. circulation interaction between deep (NSSA) shallow (TCA) detected pressure-depth profile of 27 production wells penetrating NSSA. facies evolution systems (Ca–Mg–HCO3) first stage (freshwater NSSA) changed (Na–Cl) type last (brackish TCA springs). Support vector successfully predicted rapid increase hypersaline area 22.6 km2 60.6 within 30 years, which deteriorated a large part cultivated land, reflecting environmental risk over-extraction irrigation agricultural flooding technique lack suitable drainage network. waterlogging due reduction infiltration rate (low permeability) quaternary aquifer. cause this issue could be complete saturation with chrysotile, calcite, talc, dolomite, gibbsite, chlorite, Ca-montmorillonite, illite, hematite, kaolinite K-mica (saturation index >1), giving chance these minerals precipitate pore spaces decrease rate. NSSA is appropriate irrigation, whereas inappropriate potential salinity magnesium risks. best way manage use underground drip combine
Язык: Английский