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

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• 作者：Junwei Ma ; Huiming Tang ; Xinli Hu ; Antonio Bobet ; Ming Zhang ; Tingwei Zhu…
• 关键词：Data mining ; Two ; step cluster ; Apriori algorithm ; Majiagou landslide ; Displacement ; Water level fluctuation ; Rainfall
• 刊名：Landslides
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：14
• 期：1
• 页码：311-322
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Natural Hazards; Geography, general; Agriculture; Civil Engineering;
• 出版者：Springer Berlin Heidelberg
• ISSN：1612-5118
• 卷排序：14

文摘

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.