Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models
文献类型：期刊论文
| 作者 | Yang, Zhizhou1,2; Zou, Lei1; Xia, Jun1,3; Qiao, Yunfeng2,4; Cai, Diwen1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 14期号:7页码:17 |
| 关键词 | CEEMDAN method GA-SVM model decomposition prediction water quality |
| DOI | 10.3390/rs14071714 |
| 通讯作者 | Zou, Lei(zoulei@igsnrr.ac.cn) |
| 英文摘要 | Urban water quality is facing strongly adverse degradation in rapidly developing areas. However, there exists a huge challenge to estimating the inner features and predicting the variation of long-term water quality due to the lack of related monitoring data and the complexity of urban water systems. Fortunately, multi-remote sensing data, such as nighttime light and evapotranspiration (ET), provide scientific data support and reasonably reveal the variation mechanisms. Here, we develop an integrated decomposition-reclassification-prediction method for water quality by integrating the CEEMDN method, the RF method mothed, and the genetic algorithm-support vector machine model (GA-SVM). The degression of the long-term water quality was decomposed and reclassified into three different frequency terms, i.e., high-frequency, low-frequency, and trend terms, to reveal the inner mechanism and dynamics in the CEEMDAN method. The RF method was then used to identify the teleconnection and the significance of the selected driving factors. More importantly, the GA-SVM model was designed with two types of model schemes, which were the data-driven model (GA-SVMd) and the integrated CEEMDAN-GA-SVM model (defined as GA-SVMc model), in order to predict urban water quality. Results revealed that the high-frequency terms for NH3-N and TN had a major contribution to the water quality and were mainly dominated by hydrometeorological factors such as ET, rainfall, and the dynamics of the lake water table. The trend terms revealed that the water quality continuously deteriorated during the study period; the terms were mainly regulated by the land use and land cover (LULC), land metrics, population, and yearly rainfall. The predicting results confirmed that the integrated GA-SVMc model had better performance than single data-driven models (such as the GA-SVM model). Our study supports that the integrated method reveals variation rules in water quality and provides early warning and guidance for reducing the water pollutant concentration. |
| WOS关键词 | DISSOLVED-OXYGEN ; SURFACE-WATER ; LAND-COVER ; URBAN ; DECOMPOSITION ; UNCERTAINTY ; PERFORMANCE ; CHINA ; SEWER |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304] ; National Nature Science Foundation of China[41890823] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000781573100001 |
| 出版者 | MDPI |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; National Nature Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/174148]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zou, Lei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Zhizhou,Zou, Lei,Xia, Jun,et al. Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models[J]. REMOTE SENSING,2022,14(7):17. |
| APA | Yang, Zhizhou,Zou, Lei,Xia, Jun,Qiao, Yunfeng,&Cai, Diwen.(2022).Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models.REMOTE SENSING,14(7),17. |
| MLA | Yang, Zhizhou,et al."Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models".REMOTE SENSING 14.7(2022):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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