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Slope stability prediction based on HEOA-XGBoost combined model
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Yun QI1, 2, 3, Chenhao BAI**, 2, Kai QIN4, Hongfei DUAN5, Xuping LI1, Wei WANG1, 2
China Safety Science Journal | 2025, 35(9) : 137 - 144
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China Safety Science Journal | 2025, 35(9): 137-144
Safety engineering technology
Slope stability prediction based on HEOA-XGBoost combined model
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Yun QI1, 2, 3, Chenhao BAI**, 2, Kai QIN4, Hongfei DUAN5, Xuping LI1, Wei WANG1, 2
Affiliations
  • 1School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou Inner Mongolia 014010, China
  • 2College of Coal Engineering, Shanxi Datong University, Datong Shanxi 037000, China
  • 3Editorial Office of China Safety Science Journal, China Occupational Safety and Health Association, Beijing 100029, China
  • 4China Coal Research Institute, Beijing 100013, China
  • 5School of Civil Engineering, Sun Yat-sen University, Guangzhou Guangdong 510275, China
Published: 2025-09-28 doi: 10.16265/j.cnki.issn1003-3033.2025.09.0030
Outline
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To prevent slope instability accidents, a combined model based on HEOA optimized XGBoost was proposed to predict slope stability in response to the uncertainty of slope instability and the complexity of influencing factors. First, the main controlling factors affecting slope instability were analyzed. Six key influencing factors related to slope rock mass were selected to establish a slope stability prediction index system. Second, range normalization was applied to unify the feature scales, and SMOTE was employed to balance the distribution of stability classes within the dataset. Third, the HEOA was used to optimize the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss of the XGBoost model. Finally, the prediction results of the constructed model were comprehensively evaluated using the following metrics: accuracy, precision, recall, F1 score, and Cohen's Kappa coefficient, and the model was applied to specific engineering cases. The results show that the XGBoost model optimized by HEOA achieves the best performance when the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss were 6, 0.583 8, 0.461 5, 0.584 6 and 0.024 4, respectively. Compared with other intelligent algorithms-optimized XGBoost models and single XGBoost model, the HEOA-XGBoost hybrid model shows improvements in all evaluation indicators in predicting slope stability, indicating that the model has high accuracy and generalization ability in predicting slope stability.

slope stability  /  human evolutionary optimization algorithm (HEOA)  /  eXtreme gradient boosting (XGBoost)  /  range normalization  /  synthetic minority oversampling technique (SMOTE)
Yun QI, Chenhao BAI, Kai QIN, Hongfei DUAN, Xuping LI, Wei WANG. Slope stability prediction based on HEOA-XGBoost combined model[J]. China Safety Science Journal, 2025 , 35 (9) : 137 -144 . DOI: 10.16265/j.cnki.issn1003-3033.2025.09.0030
Year 2025 volume 35 Issue 9
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doi: 10.16265/j.cnki.issn1003-3033.2025.09.0030
  • Receive Date:2025-03-20
  • Online Date:2026-07-09
  • Published:2025-09-28
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  • Received:2025-03-20
  • Revised:2025-06-12
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Affiliations
    1School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou Inner Mongolia 014010, China
    2College of Coal Engineering, Shanxi Datong University, Datong Shanxi 037000, China
    3Editorial Office of China Safety Science Journal, China Occupational Safety and Health Association, Beijing 100029, China
    4China Coal Research Institute, Beijing 100013, China
    5School of Civil Engineering, Sun Yat-sen University, Guangzhou Guangdong 510275, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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