Mshiu
AI & Data Science EATCMajiGuard AI
Problem Statement
District water authorities and rural water-service managers in Tanzania face difficulty identifying water points at high risk of service disruption and determining which ones should receive preventive maintenance or rehabilitation first. Limited maintenance resources make it difficult to prioritize interventions, while water-point failures can disrupt reliable access to water for dependent communities.
Proposed Solution
MajiGuard AI will use water-point, climate, geographic, and population data with supervised Machine Learning to predict water-service disruption risk for individual water points. The system will combine risk predictions with climate exposure and community impact to generate maintenance-priority rankings and provide actionable insights through a decision-support dashboard.
Innovation
MajiGuard AI proposes a Tanzania-focused, climate-aware decision-support approach that integrates water-service disruption risk, climate exposure, geographic conditions, and community impact to support context-aware maintenance prioritization rather than focusing only on water-point functionality.
Expected Impact
The solution aims to help water-service decision-makers identify high-risk water points earlier, prioritize limited maintenance resources, and reduce disruption to vulnerable communities through more proactive and evidence-based intervention.