An interpretable hybrid model for predicting step-like landslide displacement: a case study in the Three Gorges Reservoir

Tao Ma, Lizhou Wu (), Jianting Zhou, Hong Zhang and Huabo Xiao
Additional contact information
Tao Ma: Chengdu University of Technology
Lizhou Wu: Chongqing Jiaotong University
Jianting Zhou: Chongqing Jiaotong University
Hong Zhang: Chongqing Jiaotong University
Huabo Xiao: Chengdu University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 18, No 26, 21458 pages

Abstract: Abstract Predicting landslide displacement is essential for ensuring reliable landslide warning and forecasting. Artificial intelligence models have been used to predict the displacement of step-like landslides. However, the quantitative assessment of the influence of rainfall and reservoir water level on landslide deformation is still challenging, and the interpretability of models for step-like landslides could be further enhanced. This study proposes an interpretable hybrid model by combining Light Gradient Boosting Machine (LightGBM) with Partial Dependence Plot (PDP) and Shapley Additive exPlanations (SHAP). LightGBM-SHAP-PDP is used to predict displacement and quantitatively interpret the influential factors on displacement. LightGBM is used to predict monthly displacement of Baishuihe landslide, and SHAP and PDP jointly explain the relationship between input parameters and landslide displacement from both global and local perspectives. LightGBM-SHAP-PDP are compared with particle swarm optimization- support vector machine (PSO–SVM), extreme learning machine (ELM), and random forest (RF). The results indicate that LightGBM-SHAP-PDP outperforms other models. When the reservoir water level declines to 145 m or cumulative displacement over the past 2 months exceeds 50 mm, the displacement increases significantly. LightGBM-SHAP-PDP has excellent prediction performance and interprets quantitatively the relationship between input and displacement, which provides reference for landslide warning threshold.

Keywords: Step-like landslide; Displacement prediction; Interpretability; LightGBM-SHAP-PDP (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07638-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:18:d:10.1007_s11069-025-07638-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07638-w

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().