Citation

Lim, Zheng You and Chew, Yee Jian and Ooi, Shih Yin and Pang, Ying Han and Min, Pa Pa (2026) Integrating remote sensing and machine learning for forest fire susceptibility mapping in Peninsular Malaysia: the spatio-temporal sampling tree (STS-T). Geocarto International, 41 (1). ISSN 1010-6049

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Abstract

The field of forest fire susceptibility mapping has long been constrained by a persistent trade-off between accuracy and interpretability: black-box ensemble algorithms deliver high accuracy but operate as opaque processes, while transparent decision tree models offer strong interpretability but fail to achieve sufficient accuracy. To fill this gap, this study builds on the forest fire dataset of Peninsular Malaysia spanning 2001–2023. Using recursive feature elimination, we extracted 49 key environmental features from 7349 original attributes and proposed a novel spatiotemporal sampling tree algorithm, STS-T, which has node-level adaptive temporal selection capability and incorporates historical meteorological lag terms into its decision-making process. Validated through stratified 10-fold cross-validation and a hold-out spatial test set from Johor, the pruned STS-T model fitted with a 1-year time window reached an accuracy of 98.48% and an ROC-AUC of 0.988.

Item Type:	Article
Uncontrolled Keywords:	Statistical analysis, decision tree, Peninsular Malaysia
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 Oct 2026 01:45
Last Modified:	05 Oct 2026 01:45
URII:	http://shdl.mmu.edu.my/id/eprint/16854

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