Improving the forecast performance of landslide susceptibility mapping by using ensemble gradient boosting algorithms

Landslide is the most dangerous natural hazard in mountainous regions. Disasters due to landslides annually result in human casualties, destroyed property, and monetary damages. Landslide susceptibility maps, highlighting landslide-prone areas, can provide useful spatial information for risk managem...

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Published in:	Environment, development and sustainability Vol. 27; no. 8; pp. 18409 - 18443
Main Authors:	Ha, Hang, Bui, Quynh Duy, Tran, Dinh Trong, Nguyen, Dinh Quoc, Bui, Hanh Xuan, Luu, Chinh
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.08.2025
Subjects:	disaster preparedness Earth and Environmental Science Ecology Economic Geology Economic Growth Environment Environmental Economics Environmental Management humans landslides mountains risk management spatial data Sustainable Development Vietnam Vietnam Vietnam Landslide susceptibility Gradient boosting models Bac Kan province Landslide risk management Machine learning
ISSN:	1573-2975, 1573-2975
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Abstract	Landslide is the most dangerous natural hazard in mountainous regions. Disasters due to landslides annually result in human casualties, destroyed property, and monetary damages. Landslide susceptibility maps, highlighting landslide-prone areas, can provide useful spatial information for risk management and mitigation. These maps are required to be updated continuously because of the complexity of the landslide formation and movement processes. This underlines the need to develop and use cutting-edge machine learning algorithms to produce more landslide predictive maps. The study aimed to compare the predictive performance of advanced gradient boosting algorithms for modeling landslide susceptibility, including Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CB), and Natural Gradient Boosting (NGBoost). Fifteen landslide influencing factors were collected and selected based on the relationship between historical landslide locations and local geo-environmental characteristics. The statistical parameters were used to compare and verify the models’ predictive performance. All proposed models have excellent forecast performances, of which the CB model has the best forecast performance (AUC = 0.921), followed by the GB model (AUC = 0.915), the LightGBM model (AUC = 0.911), the NGBoost (AUC = 0.900), and the XGBoost model (AUC = 0.897). Landslide susceptibility maps created by the CB model are recommended for the Bac Kan province in Vietnam after being validated with current landslide events recorded by the Vietnam Disasters Monitoring System. There is potential for gradient boosting models and landslide susceptibility maps to improve disaster management activities in hilly regions.
AbstractList	Landslide is the most dangerous natural hazard in mountainous regions. Disasters due to landslides annually result in human casualties, destroyed property, and monetary damages. Landslide susceptibility maps, highlighting landslide-prone areas, can provide useful spatial information for risk management and mitigation. These maps are required to be updated continuously because of the complexity of the landslide formation and movement processes. This underlines the need to develop and use cutting-edge machine learning algorithms to produce more landslide predictive maps. The study aimed to compare the predictive performance of advanced gradient boosting algorithms for modeling landslide susceptibility, including Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CB), and Natural Gradient Boosting (NGBoost). Fifteen landslide influencing factors were collected and selected based on the relationship between historical landslide locations and local geo-environmental characteristics. The statistical parameters were used to compare and verify the models’ predictive performance. All proposed models have excellent forecast performances, of which the CB model has the best forecast performance (AUC = 0.921), followed by the GB model (AUC = 0.915), the LightGBM model (AUC = 0.911), the NGBoost (AUC = 0.900), and the XGBoost model (AUC = 0.897). Landslide susceptibility maps created by the CB model are recommended for the Bac Kan province in Vietnam after being validated with current landslide events recorded by the Vietnam Disasters Monitoring System. There is potential for gradient boosting models and landslide susceptibility maps to improve disaster management activities in hilly regions.
Author	Bui, Hanh Xuan Luu, Chinh Ha, Hang Tran, Dinh Trong Nguyen, Dinh Quoc Bui, Quynh Duy
Author_xml	– sequence: 1 givenname: Hang orcidid: 0000-0002-0502-1682 surname: Ha fullname: Ha, Hang email: hanght@huce.edu.vn organization: Department of Geodesy and Geomatics, Hanoi University of Civil Engineering – sequence: 2 givenname: Quynh Duy orcidid: 0000-0003-1489-8918 surname: Bui fullname: Bui, Quynh Duy organization: Department of Geodesy and Geomatics, Hanoi University of Civil Engineering – sequence: 3 givenname: Dinh Trong surname: Tran fullname: Tran, Dinh Trong organization: Department of Geodesy and Geomatics, Hanoi University of Civil Engineering – sequence: 4 givenname: Dinh Quoc surname: Nguyen fullname: Nguyen, Dinh Quoc organization: Phenikaa University – sequence: 5 givenname: Hanh Xuan surname: Bui fullname: Bui, Hanh Xuan organization: Transport Engineering Design Incorporated – sequence: 6 givenname: Chinh orcidid: 0000-0002-3128-3774 surname: Luu fullname: Luu, Chinh organization: Faculty of Hydraulic Engineering, Hanoi University of Civil Engineering
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Snippet	Landslide is the most dangerous natural hazard in mountainous regions. Disasters due to landslides annually result in human casualties, destroyed property, and...
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SubjectTerms	disaster preparedness Earth and Environmental Science Ecology Economic Geology Economic Growth Environment Environmental Economics Environmental Management humans landslides mountains risk management spatial data Sustainable Development Vietnam
Title	Improving the forecast performance of landslide susceptibility mapping by using ensemble gradient boosting algorithms
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