Data-Driven Drainage Meets Climate Reality

InflowGo slashes stormwater modeling times, helping cities plan faster and adapt to intensifying rainfall

13 Feb 2026

Across Europe, drainage engineers are racing the clouds. Heavier rainfall and tighter climate targets are forcing cities to rethink how their sewers cope with sudden downpours. Into this anxious landscape steps InflowGo, an AI-powered modelling platform developed by WaterZerv with the Technical University of Denmark. Its promise is not to replace traditional hydraulic models, but to make them quicker.

For decades, engineers have relied on physics-based hydrodynamic models to simulate how pipes and basins respond to intense storms. These remain the standard for regulatory approval and detailed design. They are also slow. Running a single scenario can take hours or even days. Exploring dozens of upgrade options, such as larger pipes, retention basins or nature-based solutions, can stall planning just when speed matters most.

InflowGo attempts to loosen that constraint. Using machine learning trained on system and monitoring data, it acts as a surrogate for high-fidelity simulations. The firm says it can approximate flows, water levels and overflow risks between 100 and 10,000 times faster than conventional runs, depending on the network. That allows utilities to test options during workshops or strategy meetings, comparing grey infrastructure with greener alternatives almost in real time.

The platform is positioned as a planning tool, not a live flood warning system. It is calibrated against observed data to keep its forecasts aligned with how networks actually behave. The aim is usability and speed, rather than regulatory substitution.

That distinction matters. European municipalities face mounting pressure to justify costly climate adaptation schemes. Faster modelling can sharpen cost-benefit analyses and improve communication with politicians and the public. But regulators remain cautious. Physics-based models still underpin formal approvals, and AI tools must prove reliable, especially under rare and extreme conditions where data are thin.

The direction of travel, however, seems clear. As rainfall intensifies, the ability to test scenarios quickly becomes a strategic asset. If digital surrogates such as InflowGo continue to perform, they may not dethrone traditional models. Instead, they could become their indispensable companions, turning static drainage plans into more agile exercises in risk management.