Evaluating logistic regression and decision tree for landslide susceptibility mapping: A comparison with heuristic methods in Indonesia

Rama, Khairut Tamam Dwi; Hendrayanto, Hendrayanto; Jaya, I Nengah Surati

doi:10.15243/jdmlm.2026.133.10307

Akses terbuka Jurnal SINTA 1

Evaluating logistic regression and decision tree for landslide susceptibility mapping: A comparison with heuristic methods in Indonesia

Journal of Degraded and Mining Lands Management · Vol. 13 No. 3 (2026) · hlm. 10307–10322

DOI: 10.15243/jdmlm.2026.133.10307
ISSN: 2502-2458, 2339-076X
Terbit: 2026-07-01
Penerbit: Brawijaya University
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

Landslides are one of the most frequent and destructive natural disasters in Indonesia, yet national susceptibility assessments still rely heavily on heuristic frameworks based on expert judgments. This study evaluated the performance and interpretability of two Machine Learning (ML) models, Logistic Regression (LR) and Decision Tree (DT), compared with the heuristic baseline for landslide susceptibility mapping in the Ciujung sub-watershed, West Java-Banten. The models were developed using the NASA Global Landslide Catalog and 13 conditioning factors at 30 m spatial resolution. The LR model passed the multicollinearity test and identified annual rainfall, soil texture, and land use as dominant factors, while DT identified the same factors as the main controller, but emphasized the role of local threshold and interactions between factors. Slope does not appear as a dominant factor due to the mudslide character that not only occurs on steep slopes, but also spreads to flatter areas as deposition zones, thereby reducing the discriminating power of slope in pixel-based modeling. The models of LR and DT performed quite well (AUC>0.9) and generated spatially limited susceptibility zones. In contrast to the heuristic baseline, which sometimes overestimates the hazard areas. The findings indicated that LR and DT frameworks are able to improve the methodological transparency, spatial accuracy, and policy relevance, offering a viable approach for evidence-based catastrophe governance.

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14 penulis · 30 tautan kepenulisan bersama · 9 lintas organisasi

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IPB University
Institut Agama Islam Negeri Kendari
Indonesian National Institute of Aeronautics and Space
Kyoto University
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Rekan penulis	Organisasi	Artikel bersama
Yulia Amirul Fata	IPB University	3
Cecep Kusmana	IPB University	2
Suria Darma Tarigan	IPB University	2
Erizal Erizal	IPB University	2
Farida Herry Susanty	Afiliasi tidak diketahui	1
Endang Suhendang	IPB University	1
Samsuri Samsuri	Institut Agama Islam Negeri Kendari	1
Kukuh Murtilaksono	IPB University	1
Nur Febrianti	Indonesian National Institute of Aeronautics and Space	1
Budi Kuncahyo	IPB University	1
Takeshi Katsumi	Kyoto University	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://jdmlm.ub.ac.id/index.php/jdmlm/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Evaluating logistic regression and decision tree for landslide susceptibility mapping: A comparison with heuristic methods in Indonesia</dc:title>
	<dc:creator>Rama, Khairut Tamam Dwi</dc:creator>
	<dc:creator>Hendrayanto, Hendrayanto</dc:creator>
	<dc:creator>Jaya, I Nengah Surati</dc:creator>
	<dc:subject xml:lang="en-US">decision tree</dc:subject>
	<dc:subject xml:lang="en-US">landslide susceptibility</dc:subject>
	<dc:subject xml:lang="en-US">logistic regression</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:description xml:lang="en-US">Landslides are one of the most frequent and destructive natural disasters in Indonesia, yet national susceptibility assessments still rely heavily on heuristic frameworks based on expert judgments. This study evaluated the performance and interpretability of two Machine Learning (ML) models, Logistic Regression (LR) and Decision Tree (DT), compared with the heuristic baseline for landslide susceptibility mapping in the Ciujung sub-watershed, West Java-Banten. The models were developed using the NASA Global Landslide Catalog and 13 conditioning factors at 30 m spatial resolution. The LR model passed the multicollinearity test and identified annual rainfall, soil texture, and land use as dominant factors, while DT identified the same factors as the main controller, but emphasized the role of local threshold and interactions between factors. Slope does not appear as a dominant factor due to the mudslide character that not only occurs on steep slopes, but also spreads to flatter areas as deposition zones, thereby reducing the discriminating power of slope in pixel-based modeling. The models of LR and DT performed quite well (AUC&amp;gt;0.9) and generated spatially limited susceptibility zones. In contrast to the heuristic baseline, which sometimes overestimates the hazard areas. The findings indicated that LR and DT frameworks are able to improve the methodological transparency, spatial accuracy, and policy relevance, offering a viable approach for evidence-based catastrophe governance.</dc:description>
	<dc:publisher xml:lang="en-US">Brawijaya University</dc:publisher>
	<dc:date>2026-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/17582</dc:identifier>
	<dc:identifier>10.15243/jdmlm.2026.133.10307</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Degraded and Mining Lands Management; Vol. 13 No. 3 (2026); 10307-10322</dc:source>
	<dc:source>2502-2458</dc:source>
	<dc:source>2339-076X</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/17582/2080</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Journal of Degraded and Mining Lands Management</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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