Comparative Analysis of Machine Learning-based Forest Fire Characteristics in Sumatra and Borneo

Ayu Shabrina; Intan Nuni Wahyuni; Arnida L Latifah

doi:10.15575/join.v8i1.1035

Authors

Ayu Shabrina School of Computing, Telkom University, Bandung, Indonesia, Indonesia
Intan Nuni Wahyuni Reseach Center for Computing, The National Research and Innovation Agency of Indonesia, Indonesia
Arnida L Latifah 1) Reseach Center for Computing, The National Research and Innovation Agency of Indonesia 2) School of Computing, Telkom University, Bandung, Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v8i1.1035

Keywords:

Carbon emission, Climate, Prediction, Random forest, Regressor

Abstract

Sumatra and Borneo are areas consisting of rainforests with a high vulnerability to fire. Both areas are in the tropics which experience rainy and dry seasons annually. The long dry season such as in 2019 triggered forest and land fires in Borneo and Sumatra, causing haze disasters in the exposed areas. This indicates that climate variables play a role in burning forests and land in Borneo and Sumatra, but how climate affects the fires in both areas is still questionable. This study investigates the climate variables: temperature, humidity, precipitation, and wind speed in relation to the fire’s characteristics in Borneo and Sumatra. We use the Random Forest model to determine the characteristics of forest fires in Sumatra and Borneo based on the climate variables and carbon emission levels. According to the model, the fire event in Sumatra is slightly better predicted than in Borneo, indicating a climate-fire dependence is more prominent in Sumatra. Nevertheless, a maximum temperature variable is seemingly an important indicator for forest and land fire in both domains as it gives the largest contribution to the carbon emission.

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