Satellite-derived turbidity and salinity mapping for desalination plant site selection using remote sensing, GIS, and machine learning

Desalination is becoming increasingly crucial in addressing freshwater scarcity, particularly in coastal and dry regions. By combining satellite-derived turbidity and salinity data with Geographic Information System (GIS) and machine learning technologies, this work creates a strong and spatially comprehensive framework for selecting desalination plant sites. Using high-resolution remote sensing to produce turbidity and salinity maps, a thorough evaluation of spatial water quality along the coast was possible. A GIS-based study combined these data with bathymetry, distance to infrastructure, land use/land cover, and coastal topography. The coastal region was classified by a Random Forest algorithm into five desalination suitability categories. Results indicate that 20% (81.03 sq. km) of the area is highly suitable, while 25% (101.29 sq. km) is moderately to fairly suitable. Conversely, 15% (60.77 sq. km) was considered less appropriate and 20% (81.03 sq. km) unfit because of reasons including high turbidity, increased salinity, or limited access. For sustainable, site-specific planning of coastal desalination infrastructure, this integrated approach emphasises the efficacy of remote sensing, GIS, and machine learning.