Predictive Analytics • 2.82M records
AI-Driven Water Quality Monitoring & Predictive Maintenance
Decision-support workflow that converts sensor signals into CCME-WQI scores, risk tiers, and anomaly alerts; deployed as API + web interface.
PythonPandas/NumPyScikit-learnXGBoostTensorFlow/KerasFastAPIStreamlitSHAP
Executive summary
Built a decision-support workflow that converts large-scale sensor data into CCME-WQI scores, risk tiers, and anomaly alerts, deployed as an API + interface to support faster triage and standardized maintenance planning.
Problem
- High-volume physicochemical readings are difficult to interpret quickly for operational decisions.
- Maintenance planning needs consistent risk tiers with explainable reasoning.
- Stakeholders need standardized outputs (WQI score + tier + alerts), not raw predictions.
Data
Scale
2.82M records
Sources
- Physicochemical sensor signals
Notes
- Outputs include CCME-WQI scoring and tier mapping for incident prioritization.
Approach
- Computed CCME-WQI and mapped it into actionable risk tiers.
- Trained multiple models for prediction tasks (update exact models/metrics later).
- Added explainability (SHAP + Integrated Gradients) to support trust and auditability.
- Deployed endpoints using FastAPI and surfaced results via a Streamlit interface.
Insights
- Standardized WQI + tier outputs improve clarity for operations teams.
- Explainability artifacts support stakeholder confidence and reporting.
- Anomaly-style alerts enable faster incident triage (replace with concrete timing later).
Impact
- Operational decision support: faster triage and clearer prioritization workflow.
- Standardized reporting: consistent scoring and tiering for maintenance planning.
- Scalable pipeline: handles million-scale datasets end-to-end.
Screenshots
