End-to-End AI Application for Water Supply Chain Optimization: NRW, Leak Detection, and Resilience Using SIOTH

Department

Advanced Solutions department — PFE 2026–2027

Project

Develop an end-to-end AI application covering the water supply chain from production and transmission to storage and distribution, with the goal of reducing Non-Revenue Water (NRW), detecting leaks and strengthening supply-chain resilience and continuity.

Main topics

• Integrate and analyze operational data from production plants, pumping stations, reservoirs and distribution networks, including flows, pressures, levels, demand and metering data.
• Integrate GIS data including network topology, pipe attributes, asset locations, elevations and DMA boundaries.
• Apply water-balance methodologies, anomaly detection, demand forecasting, spatial analytics and scenario analysis.
• Build models for NRW quantification, distinguishing real and apparent losses by zone or DMA.
• Detect and localize leaks using flow and pressure anomaly detection, Minimum Night Flow analysis and machine-learning techniques.
• Use GIS-based spatial analytics to localize losses, prioritize pipes by risk and assess disruption impact by zone.
• Develop demand forecasting models for production planning and storage management.
• Assess resilience and continuity through disruption scenarios, storage autonomy and redundancy analysis.
• Generate AI recommendations for loss reduction, leak-response prioritization and continuity actions.
• Build interactive SIOTH UHMI dashboards with GIS maps, forecasts, alerts, KPIs and recommendations.

Deliverables

Technical documentation, final report and presentation covering methodology, models, results and potential business impact.

Keywords

Water supply chain, leak detection, Non-Revenue Water, spatial analytics, water balance, demand forecasting, machine learning, supply-chain resilience, business continuity, anomaly detection, GIS, SIOTH, SIOTH UHMI, Python.