An Interpretable Probabilistic Assessment Method for Slope Stability Based on PSO-LightGBMLSS-SHAP

DOI

10.2991/978-94-6463-658-1_88How to use a DOI?

Keywords

Slope stability; Particle Swarm Optimization (PSO); LightGBMLSS; SHAP; Interpretability; Probabilistic prediction

Abstract

This study introduces a probabilistic and interpretable method for assessing slope stability using Particle Swarm Optimization (PSO), LightGBMLSS, and Shapley Additive Explanations (SHAP). The method models the probability distribution of the slope safety factor through the LightGBMLSS model, with hyperparameters optimized by PSO, and SHAP used for result interpretation. By quantifying uncertainty in slope stability, it offers reliable support for slope prevention and risk management. Validation on 100 cases shows high-precision probabilistic predictions with good interpretability, highlighting its effectiveness for practical use.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinbo Zhang
AU  - Yuanhao Chen
AU  - Yan Yan
AU  - Xiaofei Ruan
AU  - Xiaoxiang Chen
AU  - Lexuan Cao
AU  - Jianxin Gong
PY  - 2025
DA  - 2025/03/03
TI  - An Interpretable Probabilistic Assessment Method for Slope Stability Based on PSO-LightGBMLSS-SHAP
BT  - Proceedings of the 2024 10th International Conference on Architectural, Civil and Hydraulic Engineering (ICACHE 2024)
PB  - Atlantis Press
SP  - 839
EP  - 848
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-658-1_88
DO  - 10.2991/978-94-6463-658-1_88
ID  - Zhang2025
ER  -