基于改进凝聚层次聚类算法的生态环境监测采样点优选技术研究  

RESEARCH ON SELECTION OF PREFERRED ECOLOGICAL ENVIRONMENT MONITORING SAMPLING POINT BASED ON AN IMPROVED HIERARCHICAL AGGLOMERATIVE CLUSTERING ALGORITHM

作　　者：彭硕[1] 郭晨[1] 周松[2] 王博[1] 

机构地区：[1]井冈山大学电子与信息工程学院,江西吉安343009 [2]井冈山大学商学院,江西吉安343009

出　　处：《井冈山大学学报（自然科学版）》2014年第6期48-53,共6页Journal of Jinggangshan University (Natural Science)

基　　金：国家科技支撑计划项目(2012BAC11B03);江西省科技支撑计划项目(20123BBG70221)

摘　　要：随着经济的快速发展,我国的生态环境面临着越来越大的压力,对生态环境的监测和预警是维护绿色生态环境可持续发展的重要措施。获得最为理想的生态环境数据是开展生态监测和预警的前提,而合理的采样点选择是生态环境监测中一个重要环节。本文介绍了一种对采样点进行优选的方法,首先利用数据预处理技术对初始环境监测数据进行处理,之后利用基于改进凝聚层次聚类算法对环境监测数据进行聚类,最后选出距离聚类中心最近的采样点作为优选采样点。整个处理技术简单有效,对于中小规模的生态环境监测采样点的优选具有现实意义。With the rapid development of economy in our country, ecological environment is becoming more and more stressful. Environmental monitoring and early warning on the environment are important aspects to maintain the ecological green and sustainable development. In order to get the most optimal ecological environment data under limited conditions, we should carry out a reasonable selection of a preferred sampling point. Therefore, the selection of a preferred environmental monitoring sampling point is an important part of ecological environmental monitoring. Initial environment monitored data is processed first by using a series of data preprocessing techniques. Therefore, environment monitored data is clustered by using a clustering algorithm based on improved agglomerative hierarchy. Finally, a sampling point closest to the cluster center is selected as a preferred sampling point. The whole process is simple and effective and has a realistic significance for selecting a preferred sampling point during a small and medium scale ecological environment monitoring.

关 键 词：环境监测 采样点 数据聚类 凝聚层次聚类 

分 类 号：X830.1[环境科学与工程—环境工程]