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

Department

Advanced Solutions department — Project 03/03 of the Integration Objects PFE Book 2026–2027. The topic is built around the SIOTH® Industrial IT-OT-IoT platform.

Project

Develop an end-to-end AI application covering the water supply chain from production and transmission to storage and distribution. The project aims to reduce Non-Revenue Water (NRW), detect leaks, and strengthen supply-chain resilience and continuity.

Objectives and deliverables

• 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 covering network topology, pipe attributes, asset locations, elevations, and DMA boundaries using SIOTH, Python, and relevant libraries.
• Apply water-balance methodologies, anomaly detection, demand forecasting, spatial analytics, and scenario analysis.
• Develop water-balance and NRW quantification models distinguishing real and apparent losses by zone or DMA.
• Develop leak-detection and localization models using flow and pressure anomaly detection, Minimum Night Flow analysis, and Machine Learning.
• Use GIS-based spatial analytics to localize leaks and losses, prioritize pipes by risk, and assess disruption impacts by zone.
• Develop demand forecasting models supporting production planning and storage management.
• Assess supply-chain resilience and continuity through plant-outage, pipeline-failure, and demand-surge scenarios, including storage autonomy and redundancy analysis.
• Generate AI recommendations for loss reduction, leak-response prioritization, and continuity actions.
• Develop SIOTH UHMI dashboards with GIS map views, forecasts, alerts, recommendations, and KPIs including NRW percentage, ILI, MNF, supply-demand balance, storage days, and resilience index.
• Produce technical documentation, a final report, and a presentation covering methodology, models, results, and potential business impact.

Keywords

AI, Python, 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.