News

New AI system predicts how to prevent wildfires

A machine learning model can evaluate the effectiveness of different management strategies
A satellite image of Borneo and part of Malaysia covered by plumes of smoke from fires. The many fires are marked on the map as red dots.
Satellite image of Borneo in 2006 covered by smoke from fires (marked by red dots). Image: Jeff Schmaltz, MODIS Rapid Response Team / NASA.

Wildfires are a growing threat in a world shaped by climate change. Now, researchers at Aalto University have developed a neural network model that can accurately predict the occurrence of fires in peatlands. They used the new model to assess the effect of different strategies for managing fire risk and identified a suite of interventions that would reduce fire incidence by 50-76%.

The study focused on the Central Kalimantan province of Borneo in Indonesia, which has the highest density of peatland fires in Southeast Asia. Drainage to support agriculture or residential expansion has made peatlands increasingly vulnerable to recurring fires. In addition to threatening lives and livelihoods, peatland fires release significant amounts of carbon dioxide. However, prevention strategies have faced difficulties because of the lack of clear, quantified links between proposed interventions and fire risk.

The new model uses measurements taken before each fire season in 2002-2019 to predict the distribution of peatland fires. While the findings can be broadly applied to peatlands elsewhere, a new analysis would have to be done for other contexts. ‘Our methodology could be used for other contexts, but this specific model would have to be re-trained on the new data,’ says Alexander Horton, the postdoctoral researcher who carried out study.

The researchers used a convolutional neural network to analyse 31 variables, such as the type of land cover and pre-fire indices of vegetation and drought. Once trained, the network predicted the likelihood of a peatland fire at each spot on the map, producing an expected distribution of fires for the year.

Overall, the neural network’s predictions were correct 80-95% of the time. However, while the model was usually right in predicting a fire, it also missed many fires that actually occurred. About half of the observed fires weren’t predicted by the model, meaning that it isn’t suitable as an early-warning predictive system. Larger groupings of fires tended to be predicted well, while isolated fires were often missed by the network. With further work, the researchers hope to improve the network’s performance so it can also serve as an early-warning system.

The team took advantage of the fact that fire predictions were usually correct to test the effect of different land management strategies. By simulating different interventions, they found that the most effective plausible strategy would be to convert shrubland and scrubland into swamp forests, which would reduce fire incidence by 50%. If this were combined with blocking all of the drainage canals except the major ones, fires would decrease by 70% in total.

However, such a strategy would have clear economic drawbacks. ‘The local community is in desperate need of long-term, stable cultivation to booster the local economy,’ says Horton.

An alternative strategy would be to establish more plantations, since well-managed dramatically reduce the likelihood of fire. However, the plantations are among the key drivers of forest loss, and Horton points out ‘the plantations are mostly owned by larger corporations, often based outside Borneo, which means the profits aren't directly fed back into the local economy beyond the provision of labour for the local workforce.’

Ultimately, fire prevention strategies have to balance risks, benefits, and costs, and this research provides the information to do that, explains Professor Matti Kummu, who led the study team. ‘We tried to quantify how the different strategies would work. It’s more about informing policy-makers than providing direct solutions.’

The findings were published in Nature's journal Communications Earth & Environment.

Matti Kummu

Matti Kummu

Associate Professor
T213 Built Environment
  • Published:
  • Updated:
Share
URL copied!

Read more news

Image and photo by Aalto University, Giulnara Launonen. MMD logo by Aalto University, Mithila Mohan
Research & Art Published:

Multifunctional Materials Design: Highlights of 2022

Our group's milestones of the previous year
Nainen rannalla tuulisella säällä hymyilee, taustalla meri kuohuaa
Appointments, Research & Art Published:

Professor Ranja Hautamäki: ‘Diverse urban nature is key to increasing well-being and carbon sinks’

Professor of Landscape Architecture is tackling the issues of climate change mitigation and urban carbon sinks.
NASAn Curiosity-mönkijä kuvaama pölypyörre Marsin Gale-kraatterissa. Kuvankäsittely: Henrik Kahanpää. Alkuperäinen kuva: NASA / JPL-Caltech
Research & Art, Studies Published:

On Mars the weather varies dramatically, however the planet’s climate is not changing

The doctoral dissertation of Henrik Kahanpää also questions a prevailing perception related to dust devils on Mars. As a researcher, he hopes that humans would never go to Mars.
A portrait of Laureen Mahler.
Research & Art Published:

Laureen Mahler uses origami folding to create bio-based packaging that is also pleasant to look at

Many products of the packaging industry are made of plastic and other fossil-based materials. The Aalto University Bioinnovation Center is developing ecological packaging solutions based on origami folding which also have value as beautiful objects.