Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression
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DOI: 10.1007/s11069-021-04733-6
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Cited by:
- Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Ailin Cabrera & Camilo Ferro & Alejandro Casallas & Ellie Anne López-Barrera, 2024. "Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean," Sustainability, MDPI, vol. 16(8), pages 1-35, April.
- Chaoxue Tan & Zhongke Feng, 2023. "Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
- Slobodan Milanović & Zoran Trailović & Sladjan D. Milanović & Eduard Hochbichler & Thomas Kirisits & Markus Immitzer & Petr Čermák & Radek Pokorný & Libor Jankovský & Abolfazl Jaafari, 2023. "Country-Level Modeling of Forest Fires in Austria and the Czech Republic: Insights from Open-Source Data," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
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Keywords
Tropical ecosystem; Wildfire drivers; Fire prevention; Spatial fire distribution;All these keywords.
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