Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map
In order to investigate the distribution characteristics of pollutants at contaminated sites, it is necessary to collect soil and groundwater samples by drilling and test them by the standard procedure. In the preliminary and detailed investigation, a large amount of data of soil and groundwater pol...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Hydrogeology & Engineering Geology
2021-05-01
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| Series: | Shuiwen dizhi gongcheng dizhi |
| Subjects: | |
| Online Access: | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202008001 |
| _version_ | 1811169550967767040 |
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| author | Chunlong MA Xiaoqing SHI Weiwei XU Jinghua REN Pei WANG Jichun WU |
| author_facet | Chunlong MA Xiaoqing SHI Weiwei XU Jinghua REN Pei WANG Jichun WU |
| author_sort | Chunlong MA |
| collection | DOAJ |
| description | In order to investigate the distribution characteristics of pollutants at contaminated sites, it is necessary to collect soil and groundwater samples by drilling and test them by the standard procedure. In the preliminary and detailed investigation, a large amount of data of soil and groundwater pollution will be obtained. These data are often characterized by large sample size, multiple monitoring indicators and complex data structures, and how to extract valuable information from the big data has become an important research issue. This study takes an organic contaminated site as an example, and carries out big data analytics by using self-organizing map (SOM) and k-means algorithm to explore the correlation between each organic pollution indicator of groundwater and soil. The results show that (1) the big data analytics based on self-organizing map can rapidly mine the complicated multi-dimensional monitoring data of contaminated site, and extract key information effectively. (2) The pollution indicators in groundwater are characterized by significant clustering, and the indicators in the same cluster are of similar spatial distribution characteristics. In view of this, a screening strategy may classify the indicators first and then rank them, and can be adopted at contaminated site to reduce the number of pollution indicators detected and finally save the cost of site detection. (3) The pollution indicators in soil and groundwater also have strong spatial correlation, which is mainly due to the slow seepage velocity of groundwater. According to the correlation of the spatial distribution of pollution indicators in soil and groundwater, it is helpful to trace the pollution sources at contaminated sites. |
| first_indexed | 2024-04-10T16:44:23Z |
| format | Article |
| id | doaj.art-6f26709b2c7e40c298a18a161c6b8c2b |
| institution | Directory Open Access Journal |
| issn | 1000-3665 |
| language | zho |
| last_indexed | 2024-04-10T16:44:23Z |
| publishDate | 2021-05-01 |
| publisher | Editorial Office of Hydrogeology & Engineering Geology |
| record_format | Article |
| series | Shuiwen dizhi gongcheng dizhi |
| spelling | doaj.art-6f26709b2c7e40c298a18a161c6b8c2b2023-02-08T01:29:54ZzhoEditorial Office of Hydrogeology & Engineering GeologyShuiwen dizhi gongcheng dizhi1000-36652021-05-0148319120210.16030/j.cnki.issn.1000-3665.202008001202008001Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing mapChunlong MA0Xiaoqing SHI1Weiwei XU2Jinghua REN3Pei WANG4Jichun WU5Key Laboratory of Surficial Geochemistry, Ministry of Education/School of Earth Sciences and Engineering, Nanjing University, Nanjing, Jiangsu 210023, ChinaKey Laboratory of Surficial Geochemistry, Ministry of Education/School of Earth Sciences and Engineering, Nanjing University, Nanjing, Jiangsu 210023, ChinaEngineering Technology Innovation Center for Ecological Monitoring and Restoration in Arable Land, Ministry of Natural Resources/Geological Survey of Jiangsu Province, Nanjing, Jiangsu 210018, ChinaEngineering Technology Innovation Center for Ecological Monitoring and Restoration in Arable Land, Ministry of Natural Resources/Geological Survey of Jiangsu Province, Nanjing, Jiangsu 210018, ChinaChangzhou Research Academy of Environment Sciences, Changzhou, Jiangsu 213022, ChinaKey Laboratory of Surficial Geochemistry, Ministry of Education/School of Earth Sciences and Engineering, Nanjing University, Nanjing, Jiangsu 210023, ChinaIn order to investigate the distribution characteristics of pollutants at contaminated sites, it is necessary to collect soil and groundwater samples by drilling and test them by the standard procedure. In the preliminary and detailed investigation, a large amount of data of soil and groundwater pollution will be obtained. These data are often characterized by large sample size, multiple monitoring indicators and complex data structures, and how to extract valuable information from the big data has become an important research issue. This study takes an organic contaminated site as an example, and carries out big data analytics by using self-organizing map (SOM) and k-means algorithm to explore the correlation between each organic pollution indicator of groundwater and soil. The results show that (1) the big data analytics based on self-organizing map can rapidly mine the complicated multi-dimensional monitoring data of contaminated site, and extract key information effectively. (2) The pollution indicators in groundwater are characterized by significant clustering, and the indicators in the same cluster are of similar spatial distribution characteristics. In view of this, a screening strategy may classify the indicators first and then rank them, and can be adopted at contaminated site to reduce the number of pollution indicators detected and finally save the cost of site detection. (3) The pollution indicators in soil and groundwater also have strong spatial correlation, which is mainly due to the slow seepage velocity of groundwater. According to the correlation of the spatial distribution of pollution indicators in soil and groundwater, it is helpful to trace the pollution sources at contaminated sites.https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202008001self-organizing mapcontaminated sitemultiple monitoring indicatorscorrelation analysissoilgroundwater |
| spellingShingle | Chunlong MA Xiaoqing SHI Weiwei XU Jinghua REN Pei WANG Jichun WU Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map Shuiwen dizhi gongcheng dizhi self-organizing map contaminated site multiple monitoring indicators correlation analysis soil groundwater |
| title | Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map |
| title_full | Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map |
| title_fullStr | Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map |
| title_full_unstemmed | Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map |
| title_short | Correlation analysis of multiple monitoring indicators of contaminated site based on self-organizing map |
| title_sort | correlation analysis of multiple monitoring indicators of contaminated site based on self organizing map |
| topic | self-organizing map contaminated site multiple monitoring indicators correlation analysis soil groundwater |
| url | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202008001 |
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