Artificial intelligence

AI predicts flooding

The weather of the future will be significantly wetter. Artificial intelligence can be used to develop early warning systems that can buy time to mitigate flood damage and to plan good systems to divert water away.

Floods in a flat landscape where a bridge is almost submerged. Photo: Jammerbugt Kommune
Much of Jammerbugt Municipality consists of low-lying areas that are at risk of flooding in heavy and persistent rain. Pictured are flooded areas along the Ryå river. Photo: Jammerbugt Municipality


Denmark was soaked by 972.7 millimeters of precipitation in 2023, according to data from the Danish Meterological Institute, DMI. This made the year the wettest since nationwide precipitation measurements in Denmark began in 1874. The record is 24 per cent above the ten-year average for 2011-2020.

DMI’s Climate Atlas predicts predicts that the future climate in Denmark will be significantly wetter. According to the Climate Atlas' 'best guess' of what the Danish climate will look like at the end of this century, the winter months will feature 12 per cent more rain than today.

Complex nature, complex calculations

The tool – a so-called ‘wet index’ – is based on artificial intelligence trained on freely available data on dynamics that influence the risk of flooding. Data comes from satellite imagery and weather forecasts, as well as information on ground and seawater levels and the topography of the landscape.

However, the movement and accumulation of water in open landscapes are difficult to calculate because many parameters affect how water moves and accumulates. To handle this complexity, artificial intelligence was used in the development of the model behind the wet index.

By utilizing specific design principles in the construction of the model and feeding it with carefully selected data, the researchers have incorporated an understanding of water movement, distribution, and interaction with the surrounding environment, according to Roland Löwe. He is one of the developers of the wet index and an Associate Professor at DTU specializing in how water behaves.

Flooded holiday home. Photo: Jammerbugt Kommune
In the future, summer houses and agricultural land in Jammerbugt Municipality will be even more exposed to flooding following heavy and continuous rain. Photo: Jammerbugt Municipality

Both ups and downs

Jammerbugt Municipality tested the tool in 2023. The results show better than expected predictions for the wet spring months. However, during the summer period, when Denmark was almost drought-stricken, the tool incorrectly predicted flooding in the same areas that had been flooded during the rainy spring.

The incorrect predictions were due to the tool being trained with too little data from the summer months. This is because satellites cannot register water beneath vegetation and given that fx. fields are covered by plants during the summer, the data set at that time of the year is smaller.

“An early warning needs to be relatively accurate for citizens to trust the system. This is why we chose to do a trial run, where only selected citizens have checked it regularly - and where we as a municipality had drones in the air to validate predictions," explains Project Manager Heidi Egeberg Johansen from Jammerbugt Municipality.

However, she emphasizes that the overall experience is that the project partners have created a tool with great potential. Therefore, the municipality is seeking funding to re-train and possibly adjust the model, which will be offline until that work has taken place, says Heidi Egeberg Johansen.


The money for the development of the early warning system came from a pool for so-called signature projects agreed in the 2021 Finance Act. The project received DKK 5 million of the 60 million earmarked for projects that would test new technologies based on artificial intelligence in the public sector. The partners in the project were Jammerbugt Municipality, DTU Sustain, the Alexandra Institute and KMD.

Faster calculations and decisions

Accurate calculations are crucial – not only when citizens and emergency services need to get water tubes and sandbags ready, but also when, for example, municipalities need to decide how best to expand their drainage systems to handle the wetter climate of the future. Traditional simulations can easily produce rock-solid calculations of systems’ ability to divert water under different scenarios – but according to Roland Löwe, they take forever to complete.

"In practice, this means that every time planners need to analyze something, they have to hire consultants who disappear into a box for two months before they can come back with results. And that's just too inconvenient," he explains. 

To shorten the computation time while maintaining the physical accuracy, the researchers rely on scientific machine learning, a branch of artificial intelligence that combines two different approaches.

Two approaches in one

One is machine learning, where a computer figures out how to analyze a large amount of data and makes predictions without having a theoretical understanding of the phenomena it is analysing. The spam filter in your email or the facial recognition feature in your phone are examples of machine learning.

The other approach is scientific computing, which can, for example, simulate physical processes, which in this case is how water moves through a given space under the influence of several factors.

"The advantage of combining the two approaches is that you get machine learning models that have a built-in understanding of how the system is expected to behave. This helps to ensure that the models generate fast predictions that make sense physically and aren’t all over the place, which can be a problem with machine learning models," says Roland Löwe.

In a project where the professor, along with startup WaterZerv and Associate Professor at DTU Allan Peter Engsig-Karup, used scientific machine learning to predict the movement of water through drainage systems, they managed to perform calculations 100 times faster than with traditional models.

"So instead of outsourcing a project, you can gather the relevant decision-makers in a room to run the models live and get the results more or less immediately. You can then sit down and try out different options to find the best solution for a given situation," he explains.


Several ongoing projects at DTU relate to early warning systems.

In an industrial PhD project, Phillip Aarestrup is using scientific machine learning to develop a new, fast generation of models that can predict where flooding will occur in watercourses. The project partly builds on some of the insights gained in the development of the wet index. The goal is to incorporate the new models into DMI’s national warning system. DMI is working on a new system, which is expected to be completed in 2026 (website in Danish).

DTU also participates in the EU-funded research project Unmanned Airborne Water Observing System (UAWOS). In this project, DTU is helping to develop sensor technologies and methods that combine drones, satellites, and models to calculate water levels and water discharge in remote and inaccessible rivers. This will help improve flood forecasting.


Artificial intelligence is developing at an incredibly fast pace. The potential is enormous and it's hard to see where it will end.

Artificial intelligence is based on maths and logic. We know the work processes, but we don't always know how the AI arrives at a particular solution. Therefore, as researchers and society, we must make demands on the use of the technology, both in legislation and morally.

At DTU, we have a special focus on the ethical aspect of future AI solutions.

Read more in our topic about artificial intelligence.