C4rL0
AI & Data Science UDOMPump Watch
Problem Statement
Rural water pumps in Tanzania are maintained reactively a pump is only inspected after a community reports it's broken, leaving households without water for days or weeks. District water authorities lack a way to know which pumps are likely to fail before they actually do.
Proposed Solution
A machine learning model that predicts each water point's failure risk using existing pump records (age, location, extraction type, management structure), displayed on a simple risk map so maintenance teams can prioritize inspections before pumps break down.
Innovation
Shifts water point maintenance from reactive to predictive instead of waiting for failure reports, planners get a ranked, visual list of at-risk pumps ahead of time, built on real Tanzanian pump data rather than assumptions.
Expected Impact
Fewer days of water access lost per community, more efficient use of limited maintenance budgets and staff time, and a repeatable model that can scale to any district with pump inspection records.