摘要
为有效处理海量、多种类的滑坡监测数据,提出了基于数据挖掘的滑坡监测数据处理流程。以白家包滑坡为例,利用k-means算法对监测数据进行定性处理,进而利用Apriori算法分析滑坡形变的诱发因素。实验结果表明,提出的流程能有效地处理滑坡监测数据,并发掘到有助于人们认识滑坡形变及其原因的知识。
This paper proposed a technological flow based on data mining technique for coping with difficulties in processing mass and various landslide-monitoring data effectively. Baijiabao Landslide was taken as a case study to analyze the feature information of deformation and its inducing factors by utilizing Apriori algorithm right after processing monitoring data qualitatively. It is demonstrated that the proposed technological flow can process landslidemonitoring data effectively and discover useful knowledge for understanding the circumstance and inducing-factors of landslide＇s deformation.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2012年第7期869-872,881,共5页
Geomatics and Information Science of Wuhan University
基金
国家863计划资助项目(2009AA122004)
国家国土资源部三峡库区三期地质灾害防治重大科研资助项目(SXKY3-3-2)
