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

HUANG Yanpeng; WANG Yuanhao; WANG Chao; LIU Weijiang; WANG Hong; LV Guangfeng; LIN Sijie; HU Qing

doi:10.13205/j.hjgc.202206004

Volume 40 Issue 6

Sep. 2022

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(6): 31-41,47

HUANG Yanpeng, WANG Yuanhao, WANG Chao, LIU Weijiang, WANG Hong, LV Guangfeng, LIN Sijie, HU Qing. CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 31-41,47. doi: 10.13205/j.hjgc.202206004

Citation:

HUANG Yanpeng, WANG Yuanhao, WANG Chao, LIU Weijiang, WANG Hong, LV Guangfeng, LIN Sijie, HU Qing. CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 31-41,47. doi: 10.13205/j.hjgc.202206004

Citation:

HUANG Yanpeng, WANG Yuanhao, WANG Chao, LIU Weijiang, WANG Hong, LV Guangfeng, LIN Sijie, HU Qing. CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 31-41,47. doi: 10.13205/j.hjgc.202206004

PDF( 35968 KB)

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

doi: 10.13205/j.hjgc.202206004

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

Received Date: 2022-01-10
Available Online: 2022-09-01
Publish Date: 2022-09-01

Abstract

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

FullText(HTML)

References(46)

References

[1]	GLEICK P H. Water resources[J]. Encyclopedia of Climate, Weather, 1996:817-823.
[2]	周仰效,李文鹏.地下水水质监测与评价[J].水文地质工程地质, 2008(1):1-11.
[3]	左锐,石榕涛,王膑,等.地下水型水源地水质安全预警技术体系研究[J].环境科学研究, 2018, 31(3):409-418.
[4]	任黎明,秦冰,桑军强,等.石化污染场地地下水修复治理挑战与对策[J].石油炼制与化工, 2021, 52(4):119-126.
[5]	中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.地下水质量标准:GB/T 14848-2017[S]. 2017.
[6]	张佳文,张伟红,陈震,等.北京密怀顺地区地下水污染风险评价方法探究[J].环境科学学报, 2018, 38(7):2876-2883.
[7]	孙从军,韩振波,赵振,等.地下水数值模拟的研究与应用进展[J].环境工程, 2013, 31(5):9-13 ,17.
[8]	江成鑫,赵江,张洪文.复杂岩溶条件下锰矿尾矿库地下水溶质运移特征数值模拟研究[J].中国环境监测, 2021, 37(1):95-102.
[9]	BADEENEZHAD A, TABATABAEE H R, NIKBAKHT H A, et al. Estimation of the groundwater quality index and investigation of the affecting factors their changes in Shiraz drinking groundwater, Iran[J]. Groundwater for Sustainable Development, 2020, 11:100435.
[10]	SANTOS M R, ROISENBERG A, IWASHITA F, et al. Hydrogeochemical spatialization and controls of the Serra Geral Aquifer System in southern Brazil:a regional approach by self-organizing maps and k-means clustering[J]. Journal of Hydrology, 2020, 591:125602.
[11]	FRIEDEL M J. Estimation and scaling of hydrostratigraphic units:application of unsupervised machine learning and multivariate statistical techniques to hydrogeophysical data[J]. Hydrogeology Journal, 2016, 24(8):2103-2122.
[12]	MELIN P, CESAR M J, SANCHEZ D, et al. Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps[J]. Chaos Solitons&Fractals, 2020, 138:109917.
[13]	HAN H, LIU J, SHU L, et al. Local and synoptic meteorological influences on daily variability in summertime surface ozone in eastern China[J]. Atmospheric Chemistry and Physics, 2020, 20(1):203-222.
[14]	DAI L J, WANG L Q, LI L F, et al. Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China[J]. Science of the Total Environment, 2018, 621:1433-1444.
[15]	KALTEH A M, BERNDTSSON R. Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP)[J]. Hydrological Sciences Journal, 2007, 52(2):305-317.
[16]	NOURANI V, MOGADDAM A A, NADIRI A O. An ANN-based model for spatiotemporal groundwater level forecasting[J]. Hydrological Processes, 2008, 22(26):5054-5066.
[17]	IWASHITA F, FRIEDEL M J, FERREIRA F J F. A self-organizing map approach to characterize hydrogeology of the fractured Serra-Geral transboundary aquifer[J]. Hydrology Research, 2018, 49(3):794-814.
[18]	洪悦,郭承军.基于K-means和SOM的水下传感器数据采集方法[J].数据采集与处理, 2021, 36(2):280-288.
[19]	陈军林,彭润民,李帅值,等.利用自组织特征映射神经网络和K-means聚类算法挖掘区域化探数据中的地质信息[J].物探与化探, 2017, 41(5):919-927.
[20]	中华人民共和国环境保护总局.水质六价铬的测定二苯碳酰二肼分光光度法:GB/T 7467-1987[S].北京:1987.
[21]	中华人民共和国卫生部,中国国家标准化委员会.生活饮用水标准检验方法:GB/T 5750[S].北京:中国标准出版社, 2006.
[22]	American Public Health Association. 4500-NO₃-NITROGEN (NITRATE)[M]. Standard Methods For the Examination of Water and Wastewater, 2018.
[23]	中华人民共和国环境保护部.水质氨氮的测定水杨酸分光光度法:HJ 536-2009[S].北京:中国环境科学出版社, 2009.
[24]	国家环境保护总局.水和废水监测分析方法[M]. 4版.北京:中国环境科学出版社, 2002.
[25]	U.S.Environmental Protection Agency. Inductively coupled plasma-optical emission spectrometry:method 6010D[S]. 2008.
[26]	KOHONEN T. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics, 1982, 43(1):59-69.
[27]	MARINAI S, FAINI S, MARINO E, et al. Efficient word retrieval by means of SOM clustering and PCA[C]//International Workshop on Document Analysis Systems, Nelson, New Zealand, 2006.
[28]	ASTEL A, TSAKOVSKI S, BARBIERI P, et al. Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets[J]. Water Research, 2007, 41(19):4566-4578.
[29]	任婷玉,梁中耀,陈会丽,等.基于模式识别方法的湖泊水质污染特征聚类研究[J].北京大学学报(自然科学版), 2019, 55(2):335-341.
[30]	VESANTO J, ALHONIEMI E. Clustering of the self-organizing map[J]. IEEE Transactions on Neural Networks, 2000, 11(3):586-600.
[31]	JAMES G, WITTEN D, HASTIE T, et al. An introduction to statistical learning[M]. Springer, 2013.
[32]	FABBROCINO S, RAINIERI C, PADUANO P, et al. Cluster analysis for groundwater classification in multi-aquifer systems based on a novel correlation index[J]. Journal of Geochemical Exploration, 2019, 204:90-111.
[33]	DAVIES D L, BOULDIN D W. A cluster separation measure[J]. IEEE Transactions on pattern analysis and machine intelligence, 1979,2:224-227.
[34]	ZOUBI M D B A, RAWI M A. An efficient approach for computing silhouette coefficients[J]. Journal of Computer Science, 2008, 4(3):252.
[35]	SYAKUR M, KHOTIMAH B, ROCHMAN E, et al. Integration k-means clustering method and elbow method for identification of the best customer profile cluster[C]//The 2nd International Conference on Vocational Education and Electrical Engineering (ICVEE), Surabaya, Indonesia, 2017.
[36]	WANG J X, WANG Z Y, YANG C, et al. Optimization of the number of components in the mixed model using multi-criteria decision-making[J]. Applied Mathematical Modelling, 2012, 36(9):4227-4240.
[37]	TIBSHIRANI R, WALTHER G, HASTIE T. Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society:Series B (Statistical Methodology), 2001, 63(2):411-423.
[38]	栗泽苑,杨雷峰,华道柱,等. 2013-2018年中国近地面臭氧浓度空间分布特征及其与气象因子的关系[J].环境科学研究, 2021, 34(9):2094-2104.
[39]	GEHRING U, TAMBURIC L, SBIHI H, et al. Impact of noise and air pollution on pregnancy outcomes[J]. Epidemiology, 2014,25(3):351-358.
[40]	HUANG Y, LI T Q, WU C X, et al. An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils[J]. Journal of Hazardous Materials, 2015, 299:540-549.
[41]	袁建飞,邓国仕,徐芬,等.毕节市北部岩溶地下水水化学特征及影响因素的多元统计分析[J].中国地质, 2016, 43(4):1446-1456.
[42]	刘丽丽,邓一荣,廖高明,等.华南某污染场地土壤重金属污染健康风险评估与来源解析[J].环境污染与防治, 2021, 43(7):875-879.
[43]	任加国,龚克,马福俊,等.基于BP神经网络的污染场地土壤重金属和PAHs含量预测[J].环境科学研究, 2021, 34(9):2237-2247.
[44]	胡冰殊,柳西亚,王宁,等.河南省东部平原浅层地下水水质特征分析[J].地下水, 2021, 43(2):33-36.
[45]	徐腾,南丰,蒋晓锋,等.制革场地土壤和地下水中铬污染来源及污染特征研究进展[J].土壤学报, 2020, 57(6):1341-1352.
[46]	李笑诺,陈卫平,吕斯丹.国内外污染场地风险管控技术体系与模式研究进展[J].土壤学报, 2022,59(1):38-53.