Distributed Random Forest for Predicting Forest Wildfires Based on Weather Data
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64064-3_22
Reference33 articles.
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3. Aljumah, A.: IoT-inspired framework for real-time prediction of forest fire. Int. J. Comput. Commun. Control 17(3) (2022)
4. Amelia, J.P., Dupe, Z.L., Prasasti, I.: Analysis and verification of fire danger rating system (FDRS) parameters in land and forest fire in west kalimantan in 2019 and its relationship with hotspots and rainfall. In: Yulihastin, E., Abadi, P., Sitompul, P., Harjupa, W. (eds.), vol. 275, p. 247–64 (2022). https://doi.org/10.1007/978-981-19-0308-3_20
5. Bera, B., Shit, P.K., Sengupta, N., Saha, S., Bhattacharjee, S.: Forest fire susceptibility prediction using machine learning models with resampling algorithms, northern part of eastern Ghat mountain range (India). Geocarto Int. 37(26), 11756–11781 (2022)
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