基于Apriori算法的滑坡监测数据关联规则挖掘研究

Research on Mining Association Rules for Landslide Monitoring Data Based on Apriori Algorithm

摘要: 滑坡是一种常见的自然灾害，对人类生命和财产安全造成严重威胁，为了得到海量滑坡监测数据中隐藏的有价值信息，基于关联规则的数据挖掘技术在滑坡监测领域得到了广泛应用。本文以2016~2019年藏东南扎木弄巴滑坡观测数据作为研究对象，通过挖掘滑坡监测数据中的关联规则，识别出不同变量之间的关联性，并利用这些关联规则进行滑坡的监测和预测。首先介绍了Apriori算法的原理，然后使用K-means算法对滑坡监测数据中几类数据进行聚类处理，并将Apriori算法应用在研究数据集上，进行规则挖掘及分析，发现降雨量和地下水位的变化与滑坡的相关性，最终获得有意义的规则信息，了解滑坡发生的机理和规律，帮助我们更好地理解和应对滑坡灾害。

Abstract: Landslides are a common natural disaster that poses a serious threat to human life and property safety. In order to obtain valuable information hidden in massive landslide monitoring data, data mining technology based on association rules has been widely applied in the field of landslide monitoring. This article takes the observation data of the Zhamulongba landslide in southeastern Tibet from 2016 to 2019 as the research object. By mining association rules in landslide monitoring data, the correlation between different variables is identified, and these association rules are used for landslide monitoring and prediction. Firstly, the principle of Apriori algorithm was introduced. Then, K-means algorithm was used to cluster several types of landslide monitoring data, and Apriori algorithm was applied to the research dataset for rule mining and analysis. The correlation between rainfall and groundwater level changes and landslides was found, and meaningful rule in-formation was obtained to understand the mechanism and laws of landslide occurrence, helping us better understand and respond to landslide disasters.