AI predicts flooding

Susanne Nielsen realizes that it is probably only a matter of time before her parents' summer house at Slettestrand in North Jutland is affected by flooding. 

Because under the house, which is just 400 meters from the bay of Jammerbugt in the municipality of the same name, the groundwater level is now often so high that there is a risk that large amounts of rain will not be able to seep away, but rather enter the house.

"It's a concern we have if we get a lot of rain," she admits.

To give residents and decision-makers the best chance to protect themselves against flooding in the area, researchers from DTU have helped Jammerbugt Municipality develop an early warning tool. It can provide 48 hours' notice of local flooding along rivers, streams, and coastal areas in the municipality. It is the first of its kind to provide local flood warnings.

"It will give us time to react if necessary, so it will be a huge help," says Susanne Nielsen from her home in Aalborg – some 40 kilometres away from the summer house which she looks after for her parents, who live in Norway.

Facts

Wetter weather on the horizon

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.

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.

Facts

A signature project

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.