  Over the last few decades, deforestation and climate change have caused
increasing number of forest fires. In Southeast Asia, Indonesia has been the
most affected country by tropical peatland forest fires. These fires have a
significant impact on the climate resulting in extensive health, social and
economic issues. Existing forest fire prediction systems, such as the Canadian
Forest Fire Danger Rating System, are based on handcrafted features and require
installation and maintenance of expensive instruments on the ground, which can
be a challenge for developing countries such as Indonesia. We propose a novel,
cost-effective, machine-learning based approach that uses remote sensing data
to predict forest fires in Indonesia. Our prediction model achieves more than
0.81 area under the receiver operator characteristic (ROC) curve, performing
significantly better than the baseline approach which never exceeds 0.70 area
under ROC curve on the same tasks. Our model's performance remained above 0.81
area under ROC curve even when evaluated with reduced data. The results support
our claim that machine-learning based approaches can lead to reliable and
cost-effective forest fire prediction systems.
