Predicting Arsenic Contamination Hotspots in Abandoned River Bends in Bangladesh
A Machine Learning Approach
Julian Peter Biesheuvel (Student TU Delft)
M.E. Donselaar (TU Delft - Applied Geology)
Devanita Ghosh (TU Delft - Sanitary Engineering)
R. Lindenbergh (TU Delft - Optical and Laser Remote Sensing)
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Abstract
Arsenic contamination in shallow aquifers of Holocene alluvial basins is a serious health risk affecting millions of people [1]. Detection of arsenic hotspots is a slow and tedious process based on the analysis of groundwater samples. This study improves arsenic risk prediction by incorporating geomorphological features such as oxbow lakes and clay plugs into a machine learning (ML) approach. Advances in remote sensing [2], often combined with ML, enable the efficient detection of these and other proxy features, significantly reducing reliance on labour-intensive fieldwork. By combining these features with environmental and demographic data, the approach provides more accurate and cost-effective risk assessments, enabling better-targeted interventions in vulnerable regions and supporting proactive environmental monitoring.