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Volume 15
Issue 10
10.3390/geosciences15100390
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia

by
Zulfahmi Zulfahmi
1,*,
Moch Hilmi Zaenal Putra
1,
Dwi Sarah
1,
Adrin Tohari
1,
Nendaryono Madiutomo
2,
Priyo Hartanto
3 and
Retno Damayanti
4
1
Research Center for Geological Disaster—National Research and Innovation Agency (BRIN), Bandung 40135, Indonesia
2
Research Center for Mining Technology—National Research and Innovation Agency (BRIN), Bandar Lampung 35361, Indonesia
3
Research Center for Limnology and Water Resources—National Research and Innovation Agency (BRIN), Cibinong, Bogor 16911, Indonesia
4
Research Center for Geological Resources, National Research and Innovation Agency (BRIN), Bandung 40135, Indonesia
*
Author to whom correspondence should be addressed.
Geosciences 2025, 15(10), 390; https://doi.org/10.3390/geosciences15100390
Submission received: 27 July 2025 / Revised: 15 September 2025 / Accepted: 16 September 2025 / Published: 9 October 2025
Versions Notes

Abstract

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua.
Keywords: landslide susceptibility mapping; ensemble learning; random forest; XGBoost; CatBoost; SHAP (SHapley Additive Explanations); geospatial analysis; Papua (Indonesia) landslide susceptibility mapping; ensemble learning; random forest; XGBoost; CatBoost; SHAP (SHapley Additive Explanations); geospatial analysis; Papua (Indonesia)

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MDPI and ACS Style

Zulfahmi, Z.; Putra, M.H.Z.; Sarah, D.; Tohari, A.; Madiutomo, N.; Hartanto, P.; Damayanti, R. GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia. Geosciences 2025, 15, 390. https://doi.org/10.3390/geosciences15100390

AMA Style

Zulfahmi Z, Putra MHZ, Sarah D, Tohari A, Madiutomo N, Hartanto P, Damayanti R. GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia. Geosciences. 2025; 15(10):390. https://doi.org/10.3390/geosciences15100390

Chicago/Turabian Style

Zulfahmi, Zulfahmi, Moch Hilmi Zaenal Putra, Dwi Sarah, Adrin Tohari, Nendaryono Madiutomo, Priyo Hartanto, and Retno Damayanti. 2025. "GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia" Geosciences 15, no. 10: 390. https://doi.org/10.3390/geosciences15100390

APA Style

Zulfahmi, Z., Putra, M. H. Z., Sarah, D., Tohari, A., Madiutomo, N., Hartanto, P., & Damayanti, R. (2025). GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia. Geosciences, 15(10), 390. https://doi.org/10.3390/geosciences15100390

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