Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network

Author:

Shabrina Ayu1ORCID,Palupi Irma2ORCID,Wahyudi Bambang Ari2ORCID,Wahyuni Intan Nuni1ORCID,Murti Mulya Diana1ORCID,Latifah Arnida Lailatul3ORCID

Affiliation:

1. Research Center for Computing, National Research and Innovation Agency (BRIN), Indonesia

2. School of Computing, Telkom University, Indonesia

3. Research Center for Computing, National Research and Innovation Agency (BRIN), Indonesia and School of Computing, Telkom University, Indonesia

Publisher

ACM

Reference31 articles.

1. Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data

2. L. Breiman . 2001. Random forests. Machine learning 45, 1 ( 2001 ), 5–32. L. Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5–32.

3. C.  C. Chen , H.  W. Lin , J.  Y. Yu , and M.  H Lo . 2016. The 2015 Borneo fires: What have we learned from the 1997 and 2006 El Niños?Environmental Research Letters 11, 10 ( 2016 ), 104003. C. C. Chen, H. W. Lin, J. Y. Yu, and M. H Lo. 2016. The 2015 Borneo fires: What have we learned from the 1997 and 2006 El Niños?Environmental Research Letters 11, 10 (2016), 104003.

4. Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

5. Kementerian Lingkungan Hidup dan Kehutanan. [n. d.]. SiPongi Forest and Land Fires Monitoring System. Available online https://sipongi.menlhk.go.id/. Kementerian Lingkungan Hidup dan Kehutanan. [n. d.]. SiPongi Forest and Land Fires Monitoring System. Available online https://sipongi.menlhk.go.id/.

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