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Identification of causal factors for the Majiagou landslide using modern data mining methods

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Abstract

In this study, a data mining approach is proposed to investigate the hydrological causes of the Majiagou landslide, located in the Three Gorges Reservoir in China. It is possible to determine the cause-and-effect relationships between hydrological parameters and landslide movement. The data mining approach consists of two steps: first, hydrological indicators and landslide movements are discretized using the two-step cluster analysis; second, the association rule mining with the Apriori algorithm is employed to identify the contribution of each hydrological parameter to landslide movement. The results obtained suggest that deformation and later failure occurred first at the toe of the landslide and progressed upslope due to rising water level in the reservoir, prolonged heavy rainfall, and rapid drawdown in the reservoir. The proposed novel use of field data and data mining has the potential for providing procedures and solutions for an effective interpretation of landslide monitoring data.

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Acknowledgments

The author Junwei Ma thanks the China Scholarship Council for providing a scholarship for the research described in this paper, which was conducted as a Visiting Research Scholar at Purdue University. This study was financially supported by the National Basic Research Program “973” Project of the Ministry of Science and Technology of the People’s Republic of China (2011CB710604 and 2011CB710606), Key National Natural Science Foundation of China (41230637), National Natural Science Foundation of China (41572279, 41272305 and 41102195), China Postdoctoral Science Foundation (Grant Nos. 2012M521500 and 2014T70758), and Hubei Provincial Natural Science Foundation of China (Grant No. 2014CFB901). All supports are gratefully acknowledged.

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Authors and Affiliations

  1. Three Gorges Research Center for Geo-hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei, 430074, People’s Republic of China

    Junwei Ma & Huiming Tang

  2. School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Junwei Ma & Antonio Bobet

  3. Faculty of Engineering, China University of Geosciences, Wuhan, Hubei, 430074, People’s Republic of China

    Junwei Ma, Huiming Tang, Xinli Hu, Ming Zhang & Mutasim A. M. Ez Eldin

  4. CCCC Second Highway Consultants Co. Ltd, Wuhan, Hubei, 430056, People’s Republic of China

    Tingwei Zhu

  5. Wuhan Geotechnical Engineering and Surveying Co. Ltd, Wuhan, Hubei, 430032, People’s Republic of China

    Youjian Song

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  1. Junwei Ma
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Correspondence to Huiming Tang.

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Ma, J., Tang, H., Hu, X. et al. Identification of causal factors for the Majiagou landslide using modern data mining methods. Landslides 14, 311–322 (2017). https://doi.org/10.1007/s10346-016-0693-7

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