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
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.'
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System