As part of River Deep Mountain AI, we are now releasing an early version of our Open Flow Model on GitHub. Our model can help fill in current gaps in river flow data using artificial intelligence.

Understanding river flow is essential for effective catchment and river management. Flow data, typically measured in cubic meters per second (m³/s), describe the volume of water moving past a location on a river at a given time. This information is critical for predicting floods, identifying and tracing pollution sources, and managing ecosystems and water quality. However, flow data are often sparse or entirely missing, especially in smaller or remote rivers. This is largely due to the high cost of installing and maintaining monitoring stations, leaving many catchments ungauged and without direct observations. 

Catchment managers and regulators often rely on surrogate data from comparable catchments, existing paid-for datasets or conduct expensive monitoring campaigns. While these approaches can be useful, they are limited in scalability and timeliness, especially when rapid or wide-scale assessments are needed.

To overcome this challenge, we have developed a Long Short-Term Memory (LSTM) model based on a recurring neural network (RNN) architecture that integrates static and dynamic data sources to estimate daily mean river flow, without the need to install a flow-gauge. Our Open Flow Model can be used as an additional support-tool for decision-makers to get a faster and cheaper understanding of river characteristics.
 

Using more than 40 years of daily river flow data for England

To ensure transferability between rivers and catchments, we have intentionally focused our model development on open source datasets with national coverage, ensuring the development of a robust and general model for predicting river flow across England.

Our flow prediction model compiles historical flow, weather and catchment data from the CAMELS-GB dataset, the Environment Agency’s Hydrology Data Explorer, and a curated list of chalk streams from Natural England. This includes 40 years of daily river flow data from 409 catchments across England, enabling our model to understand the relationship between weather, topography, catchment attributes and river flow. 

For our model, we have implemented a dual-branch architecture. One branch incorporates a LSTM layer designed to handle time-varying variables, such as weather conditions. The other branch is an encoder processing catchment attributes, such as elevation, land cover and soil. The outputs of these two branches are concatenated and passed through a final dense layer, which produces a prediction of daily mean flow (m3/s) for any river.  

Our first iteration of the Open Flow Model still has a range of limitations, which we want to improve moving forward. 

For our second iteration of the model, we will optimise the input data required to run the model and fine-tune the model to improve the performance, especially in smaller catchments with lower magnitude flows.

 
A collaborative and open sourced approach

The overarching objective of River Deep Mountain AI is to bring key stakeholders involved in waterbody health together and collaboratively develop open source AI/ML models, that can inform effective actions to tackle waterbody pollution.  

All our models will be released in the public domain to democratise artificial intelligence and benefit the entire water sector. The first iterations of our models are released now in May 2025, and the second iterations will be released in November 2025.

Access the first iteration of our Open Flow Model open source via GitHub. 

River Deep Mountain AI is funded by the Ofwat Innovation Fund and consists of 6 core partners: Northumbrian Water, Cognizant Ocean, Xylem Inc, Water Research Centre Limited, The Rivers Trust and ADAS. The project is further supported by 6 water companies across the United Kingdom and Ireland.