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.

Machine learning predicts seasonal perchlorate risks in groundwater of the Arjunanadi River Basin, with up to 29.8% of samples at risk and high risk.

• Why it matters: Groundwater perchlorate contamination poses health threats, especially to children, highlighting the need for accurate risk assessment and effective water management strategies.
• What they did: Using multiple machine learning algorithms, including Random Forest with 71.5% accuracy, the study analyzed perchlorate levels and mapped risk areas across different seasons and years.
• The result: Findings reveal seasonal variations in risk, with significant health vulnerabilities, guiding targeted interventions like aquifer recharge to improve water quality and support sustainable development goals.