基于改进K-means算法的排水管网监测点位优化  

Optimization of Monitoring Points in Drainage Pipe Network Based on Improved K-means Algorithm

作　　者：赵文涓 程雨涵 李梅 ZHAO Wenjuan;CHENG Yuhan;LI Mei(School of Environment and Energy,Anhui Jianzhu University,Hefei,Anhui 230009,China;Hefei Institute of Public Security Research,Tsinghua University,Hefei,Anhui 230601,China)

机构地区：[1]安徽建筑大学环境与能源学院,安徽合肥230009 [2]清华大学合肥公共安全研究院,安徽合肥230601

出　　处：《环境监测管理与技术》2024年第1期79-83,共5页The Administration and Technique of Environmental Monitoring

基　　金：安徽省重点研究与开发计划“面向水污染防治的城市排水管网风险评估诊断关键技术研发及应用示范”基金资助项目(202104i07020012)。

摘　　要：为切实提高工程监测成效,合理利用资源,提出基于改进K-means算法的排水管网监测点布置优化方法。以华东区域H市排水管网为案例,以23个原始监测点的监测数据为基础,通过原始数据处理,BIRCH预聚类确定优化监测点个数和初步优化监测点,再用K-means聚类确定最终优化监测点后,输出16个保留监测点位。经验证,监测点优化后对H市排水管网的数据输出无影响。In order to effectively improve the effectiveness of engineering monitoring and rational utilization of resources,an optimization method for setting monitoring points of drainage pipe network was established based on improved K-means algorithm.Taking the drainage pipe network in H City in East China region as an example,based on the monitoring data of 23 original monitoring points,the monitoring points were preliminary optimized through raw data processing and BIRCH pre-clustering,then the final optimized monitoring points were determined by K-means clustering,and 16 retained monitoring points were output.It was proved that the optimized monitoring points had no influence on the data output of the drainage pipe network in H city.

关 键 词：监测点位优化 BIRCH聚类分析 K-means聚类分析 排水管网 

分 类 号：X832[环境科学与工程—环境工程] TP311.13[自动化与计算机技术—计算机软件与理论]