• Classified by Research Topic • Sorted by Date • Classified by Publication Type •
Predicting Forest Fire Using Remote Sensing Data And Machine Learning.
Suwei Yang, Lupascu,Massimo and Kuldeep S. Meel.
In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), February 2021.
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.
@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.},
}
Generated by bib2html.pl (written by Patrick Riley with layout from Sanjit A. Seshia ) on Sun Apr 14, 2024 11:15:51