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Landslide displacement prediction based on spatio-temporal association rule mining between target case and similar cases

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Abstract

The displacement response of landslides to external influencing factors varies according to different creep stages. However, most traditional models ignored this variation and yielded low prediction accuracy especially for mutational displacements of acceleration deformation. To overcome this drawback, this study utilized the spatio-temporal association between the similar landslide cases with sufficient monitoring data and target case to replace the correlation between landslide displacement and external influencing factors to improve the prediction capacity for the mutational displacements. In order to reduce the prediction error caused by the difference between landslide cases, weights were assigned for similar cases according to the similarity of geological and hydrological conditions between cases in realizing the final weighted average prediction for target case. The model barely needs the long-time monitoring of external influencing factors, and it can not only enrich the data through historical landslide cases, but also reduce monitoring cost for target case. The findings of this study demonstrated the presence of a novel perspective for prediction, which may reduce prediction errors caused by any significant deviations in landslide from its previous deformation behavior. Application in engineering, as well as comparing prediction results from different models, may highlight the advantages of the proposed method.

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All data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

This work is funded by the National Key R&D Program of China (2022YFC2903903) and the National Science Foundation of China (Grant Nos. U1906208, 52004053). These supports are gratefully acknowledged.

Funding

This work is funded by the National Key R&D Program of Chian (2022YFC2903903) and the National Science Foundation of China (Grant Nos. U1906208, 52004053).

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Authors and Affiliations

  1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, China

    Feng Dai & Min Ren

  2. Center for Rock Instability and Seismicity Research, Department of Mining Engineering, School of Resource and Civil Engineering, Northeastern University, Shenyang, 110819, China

    Wancheng Zhu, Kai Guan, Penghai Zhang & Feiyue Liu

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  1. Feng Dai
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  2. Wancheng Zhu
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Contributions

FD: Conceptualization, Methodology, Data curation, Writing–original draft, Software. WZ: Supervision and Editing. MR: Methodology. KG: Supervision. PZ: Funding acquisition. FL: Data curation.

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Correspondence to Wancheng Zhu.

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Dai, F., Zhu, W., Ren, M. et al. Landslide displacement prediction based on spatio-temporal association rule mining between target case and similar cases. Stoch Environ Res Risk Assess 37, 4229–4247 (2023). https://doi.org/10.1007/s00477-023-02504-2

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