Predicting Forest Fire Using Remote Sensing Data And Machine Learning
This work provides a more cost-effective and reliable forest fire prediction system for developing countries like Indonesia, which are heavily impacted by these fires.
This paper addresses the increasing problem of forest fires, particularly in Indonesian tropical peatlands, by proposing a machine-learning approach using remote sensing data. The model achieved an AUC of over 0.81, outperforming a baseline of 0.70 AUC.
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.