Extreme gradient boosting with Shapley Additive Explanations for landslide susceptibility at slope unit and hydrological response unit scales

Abstract

This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units: and slope units (SUs) and hydrological response units (HRUs), within Goesan County, South Korea. Leveraging the capabilities of the extreme gradient boosting (XGB) algorithm combined with Shapley Additive Explanations (SHAP), this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides. SUs focus on the geomorphological features like ridges and valleys, focusing on slope stability and landslide triggers. Conversely, HRUs are established based on a variety of hydrological factors, including land cover, soil type and slope gradients, to encapsulate the dynamic water processes of the region. The methodological framework includes the systematic gathering, preparation and analysis of data, ranging from historical landslide occurrences to topographical and environmental variables like elevation, slope angle and land curvature etc. The XGB algorithm used to construct the Landslide Susceptibility Model (LSM) was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation (RCV) to ensure accuracy and reliability. To ensure optimal model performance, the XGB algorithm's hyperparameters were tuned using Differential Evolution, considering multicollinearity-free variables. The results show that SU and HRU are effective for LSM, but their effectiveness varies depending on landscape characteristics. The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved. This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM. The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment, improving both predictive capabilities and model interpretability. Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.

Keywords

Landslide susceptibility mapping / Hydrological response units / Slope units / Extreme gradient boosting / Hyper parameter tuning / Shapley additive explanations

CRediT authorship contribution statement

Ananta Man Singh Pradhan: Writing - original draft, Visual-ization, Validation, Methodology, Formal analysis, Conceptualiza-tion. Pramit Ghimire: Formal analysis, Data curation. Suchita Shrestha: Writing - review & editing, Methodology, Investigation. Ji-Sung Lee: Data curation. Jung-Hyun Lee: Visualization. Hyuck-Jin Park: Writing - review & editing, Supervision, Resources, Fund-ing acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00222536).

Availability of data and materials

The data supporting this study's findings are available from the corresponding author, upon reasonable request.

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