Abstract
This study introduces an intelligent method for regional subsurface prediction using a Stacking ensemble learning approach, which incorporates K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosted Decision Trees (GBDT), and Xgboost as base classifiers, with Logistic Regression (LR) serving as the meta-classifier. Leveraging data from 1119 boreholes in Zigong City, China, this method achieves a prediction accuracy of 93%, and notably improves the prediction of weak layers, with accuracy rates ranging from 71.4% to 81.5%. This enhancement is particularly significant in areas with a random distribution of excavation and backfill. Furthermore, this study employs the SHAP method (SHapley Additive explanations) to interpret the Stacking ensemble learning model, revealing that the outputs of the base classifiers enhance the feature set for the meta-classifier, effectively addressing the insensitivity of the spatial coordinates x, y, and z as input features for lithology prediction. The findings demonstrate that the expansion of effective feature dimensions is key to the superior performance of the Stacking ensemble learning method in regional subsurface lithology prediction.
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Acknowledgments
This paper has been supported by the National Natural Science Foundation of China (Grant No. 42072339, 41702388, U19A2097), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Grant No. SKLGP2022Z006), and Everest Technology Research Proposal of Chengdu University of Technology (Grant No. 80000-2020ZF11411).
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GeoStackingPredictor Contact: 2021010113@stu.cdut.edu.cn. Program language: Python. The source codes in this paper are available for download at the link: https://github.com/Lukacdut/GeoStackingPredictor.git.
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Bai, J., Wang, S., Xu, Q. et al. Intelligent regional subsurface prediction based on limited borehole data and interpretability stacking technique of ensemble learning. Bull Eng Geol Environ 83, 272 (2024). https://doi.org/10.1007/s10064-024-03758-y
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DOI: https://doi.org/10.1007/s10064-024-03758-y