Hydrogeochemical analysis and groundwater pollution source identification based on self-organizing map at a contaminated site

被引:22
|
作者
Zhang, Yaobin [1 ,2 ,3 ]
Zhang, Qiulan [1 ,2 ,3 ]
Chen, Wenfang [4 ]
Shi, Weiwei [4 ]
Cui, Yali [1 ,2 ,3 ]
Chen, Leilei [4 ]
Shao, Jingli [1 ,2 ,3 ]
机构
[1] China Univ Geosci Beijing, Key Lab Groundwater Circulat & Environm Evolut, Minist Educ, Beijing 100083, Peoples R China
[2] China Univ Geosci Beijing, MNR Key Lab Shallow Geothermal Energy, Beijing 100083, Peoples R China
[3] China Univ Geosci Beijing, Sch Water Resources & Environm, Beijing 100083, Peoples R China
[4] Henan Prov Bur Geoexplorat & Mineral Dev, Inst Geoenvironm Survey 1, Zhengzhou 450045, Peoples R China
关键词
Self-organizing map; Contaminated sites; Hydrogeochemical groups; K-means clustering; Pollution source identification; RED-RIVER DELTA; STATISTICAL-METHODS; HETAO BASIN; WATER; QUALITY; SOM; CLASSIFICATION; VISUALIZATION; RESOURCES; SEDIMENT;
D O I
10.1016/j.jhydrol.2022.128839
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Groundwater contamination at the site has become a very serious problem. A clear understanding of the hydrogeochemical characteristics of groundwater is indispensable for pollution remediation. It requires taking a number of samples and continuous monitoring. However, it is challenging to interpret hydrogeochemical datasets with diverse compositions and wide range of concentration by linear method. In this work, combination of self-organizing map (SOM) and K-means clustering was applied to investigate the hydrogeochemical characteristics at a contaminated site. The results showed that shallow groundwater hydrogeochemical characteristics were performed by 42 neurons and were classified into 5 clusters. The NO3- in cluster 1 widely distributed in the site. The application of fertilizers led to high NO3- concentration in groundwater. Cluster 2 was dominated by Ca2+, Mg2+, Cr(VI) and NO2- and cluster 3 was characterized by TDS, Na+, Cl-, HCO3- and SO42-. Pollutants were mainly from the migration of components at the chromium slag heap under the effect of convection and dispersion. Cluster 4 was dominated by pH, As and CO32-. Furthermore, the pH with the minimum of 8.3 and the presence of CO32- in groundwater provided a favorable opportunity for arsenic enrichment. Pollutants in cluster 4 originated from rainfall leaching on the chromium slag. Moreover, the migration of components from cluster 4 to cluster 2 was also observed by SOM and numerical simulation. Cluster 5 was mainly dominated by Mn and Fe. Reduced environment and anthropogenic activities caused Fe and Mn to exceed standards. The deep groundwater characteristics were performed using 20 neurons and were identified into 4 clusters. Its contamination was due to the leakage of shallow groundwater. Finally, the Gibbs diagram and the saturation index method performed the chemistry control mechanisms of different clusters. This study demonstrated that SOM could be used to interpret nonlinear and complex contamination datasets.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Identification and control of dynamical systems using the self-organizing map
    Barreto, GA
    Araújo, AFR
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2004, 15 (05): : 1244 - 1259
  • [32] Boiling Flow Pattern Identification Using a Self-Organizing Map
    Zaborowska, Iwona
    Grzybowski, Hubert
    Mosdorf, Romuald
    APPLIED SCIENCES-BASEL, 2020, 10 (08):
  • [33] Interval Self-Organizing Map for Nonlinear System Identification and Control
    Liu, Luzhou
    Xiao, Jian
    Yu, Long
    ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT I, PROCEEDINGS, 2008, 5263 : 78 - 86
  • [34] Hydrochemical characteristics and evolution of geothermal waters in western Yunnan, China based on self-organizing map and hydrogeochemical simulation
    Li, Bo
    Wang, Guangcai
    Liu, Fei
    Shi, Zheming
    Kong, Qingmin
    Zhang, Shouchuan
    Yan, Xin
    Liao, Fu
    Guo, Liang
    Liu, Chenglong
    APPLIED GEOCHEMISTRY, 2025, 181
  • [35] Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
    Nakamura, Katsuya
    Kobayashi, Yoshikazu
    Oda, Kenichi
    Shigemura, Satoshi
    APPLIED SCIENCES-BASEL, 2023, 13 (09):
  • [36] Self-organizing map clustering analysis for molecular data
    Wang, Lin
    Jiang, Minghu
    Lu, Yinghua
    Noe, Frank
    Smith, Jeremy C.
    ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1, 2006, 3971 : 1250 - 1255
  • [37] Biosom: gene synonym analysis by self-organizing map
    Otemaier, K. R.
    Steffens, M. B. R.
    Raittz, R. T.
    Brawerman, A.
    Marchaukoski, J. N.
    GENETICS AND MOLECULAR RESEARCH, 2015, 14 (01) : 1461 - 1468
  • [38] AN EXPLOITATION OF THE SELF-ORGANIZING MAP FOR HUMAN MOTION ANALYSIS
    Kurdthongmee, W.
    Kurdthongmee, P.
    BIODEVICES 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOMEDICAL ELECTRONICS AND DEVICES, 2009, : 151 - +
  • [39] Analysis of complex systems using the self-organizing map
    Simula, O
    Alhoniemi, E
    Hollmén, J
    Vesanto, J
    PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2, 1998, : 1313 - 1317
  • [40] Stability analysis of a neural field self-organizing map
    Detorakis, Georgios
    Chaillet, Antoine
    Rougier, Nicolas P.
    JOURNAL OF MATHEMATICAL NEUROSCIENCE, 2020, 10 (01):