基于自组织神经网络和K-means的场地地下水污染特征分析与分区管控研究

黄燕鹏; 汪远昊; 王超; 刘伟江; 王宏; 吕广丰; 林斯杰; 胡清

doi:10.13205/j.hjgc.202206004

基于自组织神经网络和K-means的场地地下水污染特征分析与分区管控研究

doi: 10.13205/j.hjgc.202206004

黄燕鹏^1,2,
汪远昊³,
王超^2,3,
刘伟江⁴,
王宏^2,3,
吕广丰³,
林斯杰³,
胡清^2,3, ,

1. 哈尔滨工业大学环境学院, 哈尔滨 150090;
2. 南方科技大学环境科学与工程学院, 广东深圳 518055;
3. 南科大工程技术创新中心(北京), 北京 100083;
4. 生态环境部土壤与农业农村生态环境监管技术中心, 北京 100012

基金项目:

国家重点研发计划(2018YFC1801303,2019YFC1803900,2018YFC1800204)

详细信息

作者简介:
黄燕鹏(1993-),男,博士研究生,主要研究方向为土壤、地下水调查与修复,大数据在生态环境中的应用。11849581@mail.sustech.edu.cn

通讯作者:
胡清(1964-),女,博士,教授,主要研究方向包括大数据与环境,城市环境工程研究,绿色可持续污染场地修复等。huq@sustech.edu.cn

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出版历程
- 收稿日期: 2022-01-10
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS

1. School of Environment, Harbin Institute of Technology, Harbin 150090, China;
2. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;
3. Engineering Innovation Center of Southern University of Science and Technology, Beijing 100083, China;
4. Technical Center for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China

摘要

摘要: 该研究基于自组织神经网络(SOM)和K-means方法,以华中地区某铬渣污染场地为研究对象,探讨了SOM+K-means方法应用于场地地下水污染分区管控的可能性。通过监测数据的描述性统计分析场地地下水污染特征,发现Cr (Ⅵ)、COD_Mn、SO₄^2-、TDS、NO₃^-、NH₃-N、Mn为研究区的主要污染物。基于SOM+K-means分析挖掘,并基于空间插值方法,将研究区地下水分为4类区域,并识别出每类区域需重点关注的污染指标。结果显示:类别Ⅰ需关注NO₃^-;类别Ⅱ需关注Cr (Ⅵ)、COD_Mn、NO₃、TDS、NH₃^--N;类别Ⅲ需关注SO₄^2-;类别Ⅳ需关注Mn。该方法可较好地应用于地下水污染分区管控,对场地地下水污染防治具有指导意义。
- 机器学习 /
- 自组织神经网络 /
- K-means /
- 地下水污染 /
- 分区管控
Abstract: Based on the self-organizing maps (SOM) and K-means method,this study took a contaminated site to explore the feasibility of applying SOM and K-means method to the zoning control of groundwater pollution.Through descriptive statistical analysis of monitoring data,the groundwater pollution characteristics of the site were obtained.It was found that Cr (Ⅵ),COD_Mn,SO₄^2-,TDS,NO₃^-,NH₃-N and Mn were the main pollutants in the study area.Derived from SOM,K-means analysis and spatial interpolation,groundwater in the study area was divided into four types of areas.The pollution factors of each cluster that need to be paid attention to were identified.The results showed that NO₃^- should be paid attention to for cluster Ⅰ;Cr (Ⅵ),COD_Mn,NO₃^-,TDS and NH₃-N for cluster Ⅱ;SO₄^2- for cluster Ⅲ,and Mn for cluster Ⅳ.This method can be applied to the zoning control of groundwater pollution,which had guiding significance for the prevention and control of groundwater pollution in contamination sites.
- machine learning /
- self-organizing map /
- K-means /
- groundwater pollution /
- pollution prevention and control

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参考文献(46)

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基于自组织神经网络和K-means的场地地下水污染特征分析与分区管控研究

doi: 10.13205/j.hjgc.202206004

作者简介:
黄燕鹏(1993-),男,博士研究生,主要研究方向为土壤、地下水调查与修复,大数据在生态环境中的应用。11849581@mail.sustech.edu.cn

通讯作者:
胡清(1964-),女,博士,教授,主要研究方向包括大数据与环境,城市环境工程研究,绿色可持续污染场地修复等。huq@sustech.edu.cn

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CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS

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留言板

基于自组织神经网络和K-means的场地地下水污染特征分析与分区管控研究

doi: 10.13205/j.hjgc.202206004

作者简介: 黄燕鹏(1993-),男,博士研究生,主要研究方向为土壤、地下水调查与修复,大数据在生态环境中的应用。11849581@mail.sustech.edu.cn

通讯作者: 胡清(1964-),女,博士,教授,主要研究方向包括大数据与环境,城市环境工程研究,绿色可持续污染场地修复等。huq@sustech.edu.cn

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出版历程

CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS

计量

出版历程

目录

期刊在线

作者中心

友情链接

作者简介:
黄燕鹏(1993-),男,博士研究生,主要研究方向为土壤、地下水调查与修复,大数据在生态环境中的应用。11849581@mail.sustech.edu.cn

通讯作者:
胡清(1964-),女,博士,教授,主要研究方向包括大数据与环境,城市环境工程研究,绿色可持续污染场地修复等。huq@sustech.edu.cn