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Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm
Zhao, Yubo1,2,3; Yu, Tao1,2; Hu, Bingliang1,2; Zhang, Zhoufeng1,2; Liu, Yuyang1,2,4; Liu, Xiao1,2; Liu, Hong1,2,5; Liu, Jiacheng1,2,4; Wang, Xueji1,2; Song, Shuyao1,2,4
作者部门光谱成像技术研究室
2022-11
发表期刊REMOTE SENSING
ISSN2072-4292
卷号14期号:21
产权排序1
摘要

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R-2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

关键词water quality monitoring near-surface remote sensing machine learning algorithm ensemble learning model
DOI10.3390/rs14215305
收录类别SCI
语种英语
WOS记录号WOS:000881388100001
出版者MDPI
引用统计
被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/96245
专题光谱成像技术研究室
通讯作者Yu, Tao
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
2.Chinese Acad Sci, Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China
3.Shaanxi Normal Univ, Sch Phys & Informat Technol, Xian 710119, Peoples R China
4.Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China
5.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China
推荐引用方式
GB/T 7714
Zhao, Yubo,Yu, Tao,Hu, Bingliang,et al. Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm[J]. REMOTE SENSING,2022,14(21).
APA Zhao, Yubo.,Yu, Tao.,Hu, Bingliang.,Zhang, Zhoufeng.,Liu, Yuyang.,...&Song, Shuyao.(2022).Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm.REMOTE SENSING,14(21).
MLA Zhao, Yubo,et al."Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm".REMOTE SENSING 14.21(2022).
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