摘要
为从数据视角识别中心城区排水泵站雨天出流污染的分类特征,采用基于无监督机器学习的K-means聚类算法对上海市中心城200余座泵站的设施数据和行为数据进行指标提取和画像分析。结果表明:中心城泵站分为低频高质型泵站、高频低质型泵站、高频高污型泵站和中频中污型泵站4类画像,建议优先加强第3类高频高污型泵站和第4类中频中污型泵站的出流污染管控,并根据分群特点提出了各类画像对应的管控对策。该研究结果具有较好的解释性和应用价值,可为基于数据分析的分类管控、管网提质增效实施优先级策略制定提供参考。
Unsupervised learning with K-means clustering is used to identify pollution characteristics of urban drainage pumping outflow during wet weather.Indicators including pumping station asset property and behavior data are chosen and then profiled for over 200 pumping stations of Shanghai downtown area.It shows that these pumping stations are classified into 4 clusters including low-frequency high-concentration,high-frequency low-concentration,high-frequency high-pollution,and medium-frequency medium-pollution,and the last 2 clusters are of higher priority for pollution control measures.The method used to profile pumping stations shows reasonable results and is of great value for policymakers to deploy drainage quality improving and efficiency enhancing measures.
作者
谭琼
TAN Qiong(Shanghai Water Planning and Design Research Institute,Shanghai 200233,China)
出处
《环境工程》
CAS
CSCD
北大核心
2023年第11期64-68,共5页
Environmental Engineering
基金
上海市科委项目(21DZ1202602)。
关键词
泵站排放
雨天污染
排水画像
无监督机器学习
drainage pumping outflow
wet weather pollution
drainage profile
unsupervised learning
