针对滑坡监测数据库数据量大,进行关联规则分析需要多次扫描数据库导致运行时间长的问题,将Eclat关联规则算法引入滑坡监测数据挖掘中,通过K-means聚类法和Eclat算法对八字门滑坡的变形进行了分析。通过综合研究,选择了降雨量监测值和库水位监测值中的6种因素进行数据挖掘分析。分别挖掘了3种降雨因子和3种库水位因子与八字门滑坡多测点位移的关联性,并从八字门滑坡时空监测大数据挖掘出的全部关联规则中选择8个具有较高的置信水平的关联规则进行分析,发现降雨和库水位因素影响八字门滑坡运动的有效信息。结果表明,这种数据挖掘方法及其在监测数据研究中的高精度,有望广泛应用于库区堆积滑坡的数据分析和预测。
Abstract
In addressing the substantial data volume within landslide monitoring databases and the lengthy processing times due to multiple database scans required for association rule analysis, we introduce the Eclat association rule algorithm into landslide monitoring data mining. This approach involves analyzing the deformation of the Bazimen landslide using the K-means clustering method and the Eclat algorithm. Through comprehensive investigation, we identify six factors from rainfall monitoring values and reservoir water level monitoring values for data mining and analysis. By uncovering the correlations of three rainfall factors and three reservoir water level factors with the displacement of multiple measurement points in the Bazimen landslide, we extract eight association rules with a high confidence level from all excavated correlation rules derived from the spatiotemporal monitoring big data of the Bazimen landslide. This analysis reveals effective information of rainfall and water level influencing landslide movement. The findings indicate the potential widespread applicability of this data mining method due to its high accuracy in monitoring data research, particularly in the analysis and prediction of accumulation landslides within reservoir areas.
关键词
八字门滑坡 /
Eclat算法 /
关联规则 /
数据挖掘 /
三峡库区
Key words
Bazimen landslide /
Eclat algorithm /
association rules /
data mining /
Three Gorges Reservoir area
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基金
河北省重点研发计划项目(22375415D);2023年河北省硕士在读研究生创新能力培养项目(CXZZSS2023131)
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