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    Prediction model for the compressive strength of rock based on stacking ensemble learning and shapley additive explanations

    New search for: Wu, Luyuan
    New search for: Li, Jianhui
    New search for: Zhang, Jianwei
    New search for: Wang, Zifa
    New search for: Tong, Jingbo
    New search for: Ding, Fei
    New search for: Li, Meng
    New search for: Feng, Yi
    New search for: Li, Hui

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    Prediction model for the compressive strength of rock based on stacking ensemble learning and shapley additive explanations

    New search for: Wu, Luyuan
    New search for: Li, Jianhui
    New search for: Zhang, Jianwei
    New search for: Wang, Zifa
    New search for: Tong, Jingbo
    New search for: Ding, Fei
    New search for: Li, Meng
    New search for: Feng, Yi
    New search for: Li, Hui

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Prediction model for the compressive strength of rock based on stacking ensemble learning and shapley additive explanations

    New search for: Wu, Luyuan
    New search for: Li, Jianhui
    New search for: Zhang, Jianwei
    New search for: Wang, Zifa
    New search for: Tong, Jingbo
    New search for: Ding, Fei
    New search for: Li, Meng
    New search for: Feng, Yi
    New search for: Li, Hui

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Access

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    Document information
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    Document information
    Title:

    Prediction model for the compressive strength of rock based on stacking ensemble learning and shapley additive explanations

    Additional title:

    Bull Eng Geol Environ

    Contributors:

    Wu, Luyuan (author) / Li, Jianhui (author) / Zhang, Jianwei (author) / Wang, Zifa (author) / Tong, Jingbo (author) / Ding, Fei (author) / Li, Meng (author) / Feng, Yi (author) / Li, Hui (author)

    Published in:

    Bulletin of Engineering Geology and the Environment ; 83

    Publication date:

    2024-11-01

    ISSN:

    1435-9537

    DOI:

    https://doi.org/10.1007/s10064-024-03896-3

    Type of media:

    Article (Journal)

    Type of material:

    Electronic Resource

    Language:

    English

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