A two-point machine learning method for the spatial prediction of soil pollution
文献类型：期刊论文
| 作者 | Gao, Bingbo1,2; Stein, Alfred3; Wang, Jinfeng4 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 108页码:10 |
| 关键词 | Two point machine learning Spatial prediction Spatial heterogeneity Soil heavy metal |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2022.102742 |
| 通讯作者 | Wang, Jinfeng(wangjf@lreis.ac.cn) |
| 英文摘要 | Heavy metal soil pollution is a worldwide problem. It is affected by many natural and human factors through heterogeneous relationships. Accurate prediction at unobserved locations using a limited number of observations hence remains a challenge. This study proposes a two-point machine learning method to fully utilize the information in spatial neighbors and high-dimensional covariates to improve prediction accuracy. It models the difference between pairs of points, predicts concentration differences between observation points and unobserved points, and uses those for neighbor selection. This supervised learning method integrates both spatial autocorrelation and property similarity. Method performance, illustrated in a case study of soil Pb, confirms that our method can greatly improve prediction accuracy for different sample sizes. The improvements vary with the sample size and have a decreasing trend as the sample size increases. Compared with ordinary kriging, kriging with external drift, random forest, and random forest-based regression kriging, the average improvements on RMSE are 1.49, 0.95, 0.93 and 0.62 respectively, and on MAE are 1.29, 1.17, 0.87 and 0.65 respectively. In the future, the method may be applied to the spatial prediction of other variables of the earth system, while the supervised learning method can be adjusted to new applications. |
| WOS关键词 | HEAVY-METAL POLLUTION ; SOURCE APPORTIONMENT ; AGRICULTURAL SOILS ; HEALTH-RISKS ; REGRESSION ; MODELS ; GIS ; INTERPOLATION ; CHINA ; SCALE |
| 资助项目 | National Key R&D Program of China[2021YFE0102300] ; Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Tech-nology and Business University |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000783587500004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R&D Program of China ; Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Tech-nology and Business University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/174566]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Jinfeng |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, 17 Tsinghua East Rd, Beijing 100083, Peoples R China 2.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China 3.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Hengelosestr 99, NL-7514 AE Enschede, Netherlands 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, A11 Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Bingbo,Stein, Alfred,Wang, Jinfeng. A two-point machine learning method for the spatial prediction of soil pollution[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,108:10. |
| APA | Gao, Bingbo,Stein, Alfred,&Wang, Jinfeng.(2022).A two-point machine learning method for the spatial prediction of soil pollution.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,108,10. |
| MLA | Gao, Bingbo,et al."A two-point machine learning method for the spatial prediction of soil pollution".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 108(2022):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。