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Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China

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Abstract

Several extensive landslides have occurred in the vicinity of the Three Gorges Reservoir since its initial impoundment in June 2003. A reduction of the landslide risk is essential for the safety and security of lives and property in the region. This study analyses the deformation states of two typical colluvial landslides (the Baijiabao and Laoshewo landslides) using 6 years of monitoring data, a two-step cluster analysis, and the Apriori algorithm. The landslide displacement versus time curves exhibit step-like patterns, and the landslide deformation is highly correlated with fluctuations in the reservoir level and seasonal precipitation. To determine different types of landslide deformation, the monthly displacement curves of the colluvial landslides are classified into three types using a two-step cluster analysis: initial deformation, constant deformation, and rapid deformation. Five driving factors were selected as the antecedents for the Apriori algorithm to obtain rules that describe the relationships between the landslide deformation and the influential parameters. These factors include the cumulative precipitation over the previous month, the maximum daily precipitation during the current month, changes in the reservoir level over the previous month, cumulative increases in the reservoir level and the average reservoir level during the current month. The analytical results were validated by comparing them with observed landslide deformation characteristics using three measurement standards: support, confidence and lift. The results show that the combined method of a two-step cluster analysis with the Apriori algorithm can effectively model the landslide deformation states that are associated with the Baijiabao and Laoshewo landslides. Moreover, this method may serve as a potential reference for deformation analyses of colluvial landslides in the Three Gorges.

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Acknowledgments

We would like to thank Dr. LaMoreaux and the anonymous reviewers for their valuable comments. This study was jointly supported by the National Natural Science Foundation of China (41501470), the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, the Ministry of Land and Resources (KF-2015-01-006), the Open Fund of Hubei Province Key Laboratory of Regional Development and Environmental Response (2015(B)001), and the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University). The authors would also like to thank the members of the Administration of Prevention and Control of Geo-Hazards in the Three Gorges Reservoir of China for their assistance during the data collection.

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Authors and Affiliations

  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Xueling Wu & Kaixiang Zhang

  2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, 518034, China

    Xueling Wu

  3. Department of Geography, Texas Center for Geographic Information Science, Texas State University, San Marcos, TX, 78666, USA

    F. Benjamin Zhan

  4. School of Engineering, China University of Geosciences, Wuhan, 430074, China

    Qinglu Deng

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  1. Xueling Wu
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  2. F. Benjamin Zhan
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  4. Qinglu Deng
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Correspondence to Qinglu Deng.

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Wu, X., Benjamin Zhan, F., Zhang, K. et al. Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China. Environ Earth Sci 75, 146 (2016). https://doi.org/10.1007/s12665-015-5022-2

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