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| Soil environmental quality zoning based on the Gaussian mixture model |
| Received:December 27, 2020 |
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| KeyWord:environmental quality zoning;Gaussian mixture model;spatial clustering;geographical detector;heavy metals in soil |
| Author Name | Affiliation | E-mail | | LIU Jiabin | South China Agricultural University, Guangzhou 510642, China
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China | | | GAO Yunbing | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China | gybgis@163.com | | LI Yongtao | South China Agricultural University, Guangzhou 510642, China | | | XU Zhuokui | School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China | | | PAN Yuchun | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China | | | YANG Jing | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China | | | ZHANG Wanqiu | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
China University of Mining and Technology-Beijing, Beijing 100083, China | |
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| Abstract: |
| Because of the comprehensive effects of natural environmental factors and human activities, soil environmental quality exhibits spatial heterogeneity. Environmental quality zoning based on sampling points is of concern in the management of soil environmental quality. In this study, a soil environmental quality zoning method based on sampling points and auxiliary factors was proposed. Based on the main influencing factors of soil environmental quality measured by geographic detectors, attribute clustering of soil sampling points was conducted using the Gaussian mixture model. Clustering results were dynamically adjusted in combination with rivers and roads to form environmental quality zones. This method was verified using sampling data for the Shunyi District of Beijing as an example. The results showed that the zoning results of this method were better than those of SOFM and K-means clustering, and were suitable for environmental quality zoning under the comprehensive influence of human and natural activities. |
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