Citation

Chew, Yee Jian and Ooi, Shih Yin and Pang, Ying Han (2025) Performance comparison of machine learning algorithms for forest fire detection in Peninsular Malaysia. In: 10th International Conference on Multimedia and Image Processing, ICMIP 2025, 26 - 28 April 2025, Okinawa, Japan.

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Abstract

This paper presents a pilot study evaluating 13 machine learning classifiers for detecting forest fires in Peninsular Malaysia using a forest fire inventory dataset generated through the Google Earth Engine (GEE) framework, developed in our previous work. The experimental results demonstrate the suitability of machine learning techniques for forest fire detection in the region. Among the classifiers tested, tree-based models outperformed others, with Random Forest achieving the highest recall of 99.7876%, followed closely by Gradient Boosting with a recall of 99.7345%. These findings suggest that tree-based classifiers are particularly well-suited for forest fire detection tasks. Future work is recommended to focus on refining or enhancing these models to further improve detection performance.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 07:33
Last Modified:	04 Oct 2025 09:18
URII:	http://shdl.mmu.edu.my/id/eprint/14597

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