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Garantizar la disponibilidad y la gestión sostenible del agua (ODS 6) es particularmente difícil en regiones secas como Rajshahi, Bangladesh, donde las comunidades dependen en gran medida de las aguas subterráneas con un potencial de recarga limitado. Cuestiones como la disminución de los niveles de agua y la contaminación por hierro, arsénico y cloruro comprometen tanto la satisfacción del usuario como la salud pública.
This study aimed to assess groundwater quality risks through regional mapping to guide the installation depth of new water sources. In collaboration with the Department of Public Health Engineering (DPHE), data were collected from 7,388 tube wells across nine upazilas, including well depth, geographic coordinates, and contaminant concentrations. Water quality was evaluated against World Health Organization and Bangladesh standards.
Machine learning (XGBoost) and spatial analysis were applied to model contaminant levels based on location and well depth. An initial model showed poor performance, but after identifying and correcting key errors, the refined model yielded significant improvements: R² increased from 0.0345 to 0.62 for iron, from −0.0015 to 0.38 for arsenic, and from 0.12 to 0.71 for chloride.