基于机器学习和聚类分析的地下水脆弱性与污染源空间关系研究

边浩博; 陈坚; 杨瑞杰; 周睿; 张涛; 黄国鑫

doi:10.13205/j.hjgc.202512013

基于机器学习和聚类分析的地下水脆弱性与污染源空间关系研究

doi: 10.13205/j.hjgc.202512013

边浩博^1,2,
陈坚²,
杨瑞杰³,
周睿¹,
张涛⁴,
黄国鑫^2, ,

1. 吉林大学新能源与环境学院, 长春 130021;
2. 生态环境部环境规划院, 北京 100041;
3. 韶关市生态环境监测站, 广东韶关 512000;
4. 生态环境部固体废物与化学品管理技术中心, 北京 100029

基金项目:

国家重点研发计划（2023YFC3708901）

详细信息

作者简介:
边浩博（2001—），男，硕士研究生，主要研究方向为土壤和地下水污染治理。2265608881@qq.com

通讯作者:
黄国鑫（1980—），男，研究员，主要研究方向为土壤和地下水污染防治。huanggx@caep.org.cn

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出版历程
- 收稿日期: 2024-12-02
- 录用日期: 2025-01-12
- 修回日期: 2024-12-28
- 网络出版日期: 2026-01-09

A method for spatial correlations between groundwater vulnerabilities and pollution sources based on machine learning and cluster analysis

1. College of New Energy and Environment, Jilin University, Changchun 130021, China;
2. Chinese Academy of Environmental Planning, Beijing 100041, China;
3. Shaoguan Ecological Environmental Monitoring Station, Shaoguan 512000, China;
4. Solids Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China

摘要

摘要: 为解决地下水脆弱性与污染源之间的空间关系信息缺乏，导致地下水污染风险管控效果较差的问题，以广东省某工业化城市为研究区，耦合了遗传算法（GA）、反向传播神经网络（BPNN）、核密度估计（KDE）和双变量局部莫兰指数（BLMI），建立了地下水脆弱性与工业污染源的空间相关关系分析方法。在该方法中，利用GA-BPNN-DRASTICL模型确定地下水脆弱性空间分布图，降低DRASTICL模型指标权重的主观性；利用KDE生成工业污染源空间分布；利用BLMI生成地下水脆弱性与工业污染源的空间聚类图，明确地下水脆弱性与工业污染源的空间集聚类型与分布；并针对性地提出了不同聚类区域应采取的风险管控措施。结果表明：当训练函数为trainlm，隐层神经元数为6，学习率为0.1，种群大小为40，交叉概率为0.6，变异概率为0.1时，GA-BPNN算法的准确率最高，相应地地下水埋深、净补给量、含水层介质、土壤介质、地形、包气带介质、渗透系数和土地利用类型的最优权重分别为2.84、5.27、0.84、2.20、2.36、6.58、1.21、6.70。高、极高脆弱性等级区域主要集中在研究区的中部和南部，工业污染源主要位于研究区的中西部，高-高聚类类型主要分布在研究区的中部、东北部和东南部。
- 地下水脆弱性 /
- 工业污染源 /
- 遗传算法 /
- 反向传播神经网络 /
- 双变量局部莫兰指数
Abstract: The efficacy of groundwater pollution risk control is often limited by a lack of information on spatial correlations between groundwater vulnerabilities and pollution sources. To overcome this limitation， a method was established to reveal the spatial correlations between groundwater vulnerabilities and industrial pollution sources in an industrialized city of Guangdong Province， China， using a combination of genetic algorithm （GA）， back propagation neural network （BPNN）， kernel density estimation （KDE）， and bivariate local Moran’s I （BLMI）. The subjectivity in the indicator weighting of a DRASTICL model was successfully reduced by GA-BPNN， and then the groundwater vulnerability map was created by the GA-BPNN-DRASTICL model. A spatial distribution map of the industrial pollution source distribution was produced by KDE. The spatial clustering map between groundwater vulnerabilities and industrial pollution sources was generated by BLMI， explicitly showing their distribution characteristics and implying that specific measures should be taken to control risks of groundwater pollution in different parts of the study area. The results showed that the best accuracy of the GA-BPNN algorithm was obtained， with the training function of trainlm， the neuron number of 6 in the hidden layer， the learning rate of 0.1， the population size of 40， the crossover rate of 0.6， and the mutation rate of 0.01. The best indicator weights for depth to the water table， net recharge， aquifer medium， soil medium， topography， impact of vadose zone， hydraulic conductivity， and land use were 2.84， 5.27， 0.84， 2.20， 2.36， 6.58， 1.21， and 6.70， respectively. The high and very high vulnerability classes were concentrated in the central and southern parts of the study area. The largest hotspot of the industrial pollution sources was located in the midwest part， and the high-high areas were mainly distributed in the central， northeast， and southeast parts.
- groundwater vulnerabilities /
- industrial pollution sources /
- genetic algorithm /
- back propagation neural network /
- bivariate local Moran’s I

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