Application of machine learning algorithms for seasonal and annual perchlorate risks in groundwater of the Arjunanadi River Basin (India): drinking water quality assessment and human vulnerability.

This research supports the modeling of seasonal and yearly perchlorate contamination risks in the groundwater of the Arjunanadi River Basin, India. Perchlorate is a new contaminant that can lower groundwater quality and threaten public health. The project framework directs the use of several machine‑learning algorithms to predict how perchlorate spreads over different seasons and years. The approach includes: – Organizing spatial and temporal hydrogeochemical data sets – Pre‑processing water‑quality parameters – Testing predictive models such as Random Forest, Support Vector Machines, and Gradient Boosting In addition, the research adds a full drinking‑water quality assessment and a human‑health vulnerability index. These tools measure exposure risks for various demographic groups. By building

strong computational models, the project helps locate key contamination hotspots and reveals the environmental factors that drive perchlorate movement. The guidance also aids in creating predictive tools that water‑resource managers can use to plan targeted clean‑up actions and protect public health in the affected areas.

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