AI Takes Over Water Management in Russia’s Belgorod Region
Over the past three years, deploying artificial intelligence to forecast both planned and emergency water outages in Russia’s Belgorod Region has delivered measurable results. What began as a digital experiment has evolved into an operational tool that now underpins the region’s water utility management.
The technology is built around the proekt Tsifrovoy Vodokanal (Digital Water Utility), which created a full digital twin of the region’s water distribution network. Using historical system data combined with real-time inputs from sensors installed at more than 230 facilities in the city of Belgorod and parts of the surrounding Belgorod district, a neural network continuously assesses infrastructure conditions and generates actionable recommendations for operators.
In practice, the AI system predicts both accidents and required planned shutdowns with accuracy exceeding 90 percent. Within the digital twin environment, operators can see the volume of water trapped in a damaged pipe segment, identify which valves must be closed and automatically generate a list of addresses where water service will be temporarily restricted.
As a result, the duration of extended repairs lasting more than eight hours has been reduced by 23 percent. Direct cost savings have already exceeded 9 million rubles, or approximately $108,000. The gains have been tangible not only for residents who face fewer disruptions, but also for the regional budget.
From Regional Pilot to Federal Model
Belgorod’s experience has drawn attention at the federal level. The solution has been included among 350 AI systems, representing more than 430 use cases, on the platform Tsifrovoy Region (Digital Region). The portal was launched at the initiative of the Russian government to facilitate knowledge sharing and scale proven AI deployments across regions.
Given the nationwide push toward digitalization and the documented performance of the system, further rollout to other Russian regions is likely. Today, Moscow, Sakhalin, Chelyabinsk and Novosibirsk regions, along with the Yamalo-Nenets Autonomous Okrug, lead in adopting AI-based public utility solutions.
From Paper-Based Processes to Data-Driven Operations
AI deployment in Belgorod did not happen overnight. The regional water utility began large-scale digital transformation in 2022 in partnership with Rosatom Infrastructure Solutions. The modernization plan was structured in four stages.
First, the utility automated its service request management system and began installing smart metering devices at key water supply facilities. At the same time, it rolled out the automated system Tsifrovoy Vodokanal (Digital Water Utility), equipping pumping stations, water treatment facilities and intake wells with the necessary digital hardware and monitoring tools.
Next, engineers completed commissioning work and launched a unified dispatch control system. The final stage involved scaling the platform and integrating advanced analytics modules.
Even before full AI functionality was activated, the digital upgrades reduced electricity consumption by 15 percent and lowered the number of system failures by 5 percent.
A Structural Shift in Utility Management
Success of Belgorod’s project demonstrates that AI-enabled systems can deliver measurable performance gains in critical infrastructure. For utilities, this translates into lower operating costs, faster incident response and improved reliability of essential services.
For Russia’s IT sector, the results signal sustained demand for advanced analytics platforms in the coming years, along with opportunities to scale solutions from regional pilots to nationwide deployments. If such systems prove effective within the complex operational environment of Russian housing and utility services, they may also find applications in other countries facing aging infrastructure and constrained budgets.
Digital transformation in the utility sector is no longer a trend-driven initiative. It is an operational necessity capable of delivering meaningful cost savings, improving service quality and strengthening infrastructure resilience.
