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@inproceedings{YLM21,
  title={Predicting Forest Fire Using Remote Sensing Data And Machine Learning},
  author={Yang, Suwei and Lupascu,Massimo and Meel, Kuldeep S.},
  booktitle=AAAI,
  month=feb,
  year={2021},
  bib2html_rescat={Misc},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={../Papers/aaai21-ylm.pdf},
  abstract={
    Over the last few decades, deforestation and climate change have caused
    increasing number of forest fires. In SoutheastAsia, 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.
  },
}