AI-powered reservoir management system

About the client

The company is a municipal water utility responsible for managing pump stations and reservoirs in a large urban area. Its main goal is to minimize flood risks during heavy rainfalls and to improve the efficiency of the city’s water infrastructure.

How it all started

In 2021, the client engaged us to develop an innovative solution that could autonomously manage pump station operations using real-time and predictive data. The main purpose was to reduce the risk of overloading drainage systems in a changing climate.

At the same time, the solution had to be scalable and reliable enough to handle a growing network of sensors across the urban landscape.

Challenges we faced

To make sure the system worked correctly and provided accurate forecasts, it was important for us to enable it to handle huge amounts of data coming from hundreds of sensors, monitoring tools and external sources like weather services.

Besides that, to keep a stable connection with the physical infrastructure, the solution had to support low-level protocols and handle hardware problems such as power cuts or equipment breakdowns.

Implemented solution

Within this project, we developed a complex solution for intelligent management of urban water infrastructure. It is based on a hybrid AI model that combines neural networks and reinforcement learning, capable of predicting potential emergencies in real time.

Our team also developed a streaming platform that processes data from hundreds of sensors with high speed and reliability. Moreover, the entire system is fully automated through a CI/CD/CD pipeline, which ensures seamless updates and deployments.

Solution’s key attributes

• Control and transparency
  The system provides centralized management and tracking of all data processing and decision-making stages, which ensures a high level of control and security, as well as simplifies diagnostics and failure elimination.
• High real-time performance
  With GPU acceleration and parallel processing, the system consistently manages high workloads and processes large volumes of data in real time, supporting hundreds of connected devices at once.
• Scalability
  Thanks to its modular architecture and support for horizontal scaling, the system can smoothly adapt to growing loads without interrupting existing operations or downtime.
• Fail-safe design
  The system is built using fault-tolerant components that ensure its stable operation even in case of hardware failures or network disruptions.

Security and privacy

To ensure system security, we isolated computing environments using containers and Kubernetes, which enabled better resource management and reduced the risk of failures. Meanwhile, all data between the sensors and the cloud is transmitted in encrypted form, protecting the system from external interference.

Additionally, the system includes log audit and model traceability functions, which helps to monitor each step in prediction and decision-making. In case of failures or unstable model behavior, protective mechanisms are automatically triggered: control is handed over to the operator, and the system reverts to a safe configuration.

Testing

The solution was tested in several stages. In the first stage, we ran simulations, analyzing the accuracy of the system’s forecasts based on weather and water level data from previous years. In the second stage, we moved on to real-world testing using sensors and pumping equipment to ensure that everything was working reliably.

To validate fault tolerance, we also simulated a range of failures, including network issues, system overloads and equipment shutdowns.