Capitalizing the Predictive Potential of Machine Learning to Detect Various Fire Types Using NASA's MODIS Satellite Data for the Mediterranean Basin
Author:
Affiliation:
1. Department of Information Systems and Technologies, Bilkent University, Turkiye
2. Department of Computer Science, University of Milan, Italy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3633598.3633603
Reference14 articles.
1. Jmendenhall “Wildfire's impact on our environment ” Utah Department of Environmental Quality https://deq.utah.gov/communication/news/wildfires-impact-on-our-environment#:∼:text=Wildfires%20can%20affect%20the%20physical significant%20increase%20in%20stormwater%20runoff (accessed Aug. 28 2023).
2. “Wildfires,” World Health Organization, https://www.who.int/health-topics/wildfires#tab=tab_1 (accessed Aug. 28, 2023).
3. B. Dunbar “Climate models project increase in U.S. wildfire risk ” NASA https://www.nasa.gov/topics/earth/features/climate-fire.html
4. A Brief Review of Machine Learning Algorithms in Forest Fires Science
5. Piyush Jain Sean C.P. Coogan Sriram Ganapathi Subramanian Mark Crowley Steve Taylor Mike D. Flannigan “A review of machine learning applications in wildfire science and management ” Environmental Reviews (2020) https://doi.org/10.1139/er-2020-0019
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