PS-04Climate, Water & Circularity
JalRakshak
AI-Based Water Quality Anomaly Detection and Response Prioritisation
SDG 6SDG 11
The challenge
Communities may continue using unsafe water because contamination is detected late, test results are scattered or authorities cannot quickly identify which supply point requires attention first. Design a water-quality monitoring and decision-support platform that combines parameters such as pH, turbidity, TDS, residual chlorine, conductivity and complaint patterns. The system should detect abnormal readings, estimate confidence, map affected locations and prioritise verification or response. It should distinguish between a sensor anomaly and a confirmed public-health event and avoid declaring water unsafe from a single unverified reading.
What a working submission needs
Build a dashboard prototype for multiple water points using open or synthetic sensor data.
It should flag anomalies, explain the triggering parameters, visualise risk on a map, create a verification/inspection queue and track resolution.
Stretch goals
sensor-health detection, contamination-source clues, short-term quality forecasting or multilingual public alerts after authorised verification.
Optional. A working core counts for more than feature count.Suggested stack
Web/mobile application, AI/ML models, a mapping or dashboard interface, and open APIs as appropriate to the problem.
Scope and responsibility
Healthcare, safety and public-sector challenges are decision-support tools, not autonomous clinical, legal, emergency-command or enforcement systems. Name your prototype’s limitations in the demo. Judging weighs a working core, justified use of AI, technical quality, real-world impact and responsible design above feature count.
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