Jiang, Bo1,2,3; Liu, Hailong1,2,3; Xing, Qianguo1,2,3
; Cai, Jiannan4; Zheng, Xiangyang1,2,3; Li, Lin1,2,3; Liu, Sisi4; Zheng, Zhiming4; Xu, Huiyan4; Meng, Ling1,2,3发表期刊WATER
2021-03-01
卷号13期号:5页码:13
关键词in situ reflectanceretrieval modelschlorophyll-atotal suspended particulateeutrophic and turbid waterthe Pearl River Delta
DOI10.3390/w13050650
通讯作者Xing, Qianguo(qgxing@yic.ac.cn)
英文摘要In order to use in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN). The results of the laboratory analysis with the water samples show that the Chl-a ranges from 3 to 256 mu g center dot L-1 with an average of 39 mu g center dot L-1 while the TSP ranges from 8 to 162 mg center dot L-1 and averages 42.5 mg center dot L-1. Ninety sets of 135 samples are used as training data to develop the retrieval models, and the remaining ones are used to validate the models. The results show that the proposed band ratio models, the three-band combination models, and the corresponding BPNN models are generally successful in estimating the Chl-a and the TSP, and the mean relative error (MRE) can be lower than 30% and 25%, respectively. However, the BPNN models have no better performance than the traditional empirical models, e.g., in the estimation of TSP on the basis of the reflectance at 555 and 750 nm (R555 and R750, respectively), the model of BPNN (R555, R750) has an MRE of 23.91%, larger than that of the R750/R555 model. These results suggest that these traditional empirical models are usable in monitoring the optically active water quality parameters of Chl-a and TSP for eutrophic and turbid waters, while the machine learning models have no significant advantages, especially when the cost of training samples is considered. To improve the performance of machine learning models in future applications on the basis of ground sensor networks, large datasets covering various water situations and optimization of input variables of band configuration should be strengthened.
资助机构Chinese Academy of Science Strategic Priority Research Program-the Big Earth Data Science Engineering Project; Instrument Developing Project of the Chinese Academy of Sciences; National Natural Science Foundation of China; project ofWater Quality Hyperspectral Monitoring and Analysis - Zhongshan Ecology and Environmental Agency
收录类别SCI
语种英语
研究领域[WOS]Environmental Sciences & Ecology; Water Resources
WOS记录号WOS:000628613700001
引用统计被引频次：5[WOS][WOS记录][WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/27351
专题中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
中科院海岸带环境过程与生态修复重点实验室
通讯作者Xing, Qianguo作者单位1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Ecol & Environm Agcy, Zhongshan 528403, Peoples R China
推荐引用方式
GB/T 7714Jiang, Bo,Liu, Hailong,Xing, Qianguo,et al. Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters[J]. WATER,2021,13(5):13.
APAJiang, Bo.,Liu, Hailong.,Xing, Qianguo.,Cai, Jiannan.,Zheng, Xiangyang.,...&Meng, Ling.(2021).Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters.WATER,13(5),13.
MLAJiang, Bo,et al."Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters".WATER 13.5(2021):13.
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