Abstract
Data-driven stability prediction of slopes on the basis of survey data plays a vital role in geohazard prevention. A critical issue in data-driven stability prediction is that many factors can affect slope stability and have varied influences. Exploring the influence of different factors on the prediction model is helpful to improve its accuracy. In this paper, we used machine learning methods to predict soil slope stability based on soil slope survey data from four cities in Hunan Province, and evaluated the effects of groundwater and lithology on soil slope stability prediction. First, we analyzed and selected features using machine learning methods, i.e., random forest combined with SHapley Additive exPlanation (SHAP) values. Second, we constructed four machine learning models and compared the performance of the models. Finally, the best machine learning model was selected, and the influence of groundwater and lithology on the prediction of soil slope stability in the study area was explored using the SHAP method. The results show that the prediction accuracy and recall rate of the model decrease when only lithology is considered, while the prediction accuracy cannot be improved when only groundwater is considered. However, when combined with lithology, the prediction performance can be improved, and the accuracy and recall rate of the model are both improved by 0.01, and F-measure is improved by 0.02. The results of this paper can help improve the prediction accuracy of soil slopes in geohazard prevention.
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Funding
This work was supported by the Natural Science Foundation of China (Grant Numbers 42277161 and 42207215) and the Fundamental Research Funds for the Central Universities (2652018091).
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Gao, W., Zang, M. & Mei, G. Exploring influence of groundwater and lithology on data-driven stability prediction of soil slopes using explainable machine learning: a case study. Bull Eng Geol Environ 83, 2 (2024). https://doi.org/10.1007/s10064-023-03466-z
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DOI: https://doi.org/10.1007/s10064-023-03466-z