Hou Yao-bin1,2; Feng Wei-wei1,2
; Cai Zong-qi1; Wang Huan-qing1; Liu Zeng-dong3发表期刊SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
2020-10-01
卷号40期号:10页码:3211-3216
关键词UltravioletAbsorption spectroscopySeawaterNitrateNeural networkSpectral analysis
DOI10.3964/j.issn.1000-0593(2020)10-3211-06
通讯作者Feng Wei-wei(wwfeng@yic.ac.cn)
英文摘要Nitrate concentration is an important indicator for the marine ecosystem. Compared with laboratory chemical methods such as Cadmium-Reduction method, in-situ nitrate optical sensor is much faster and reagent-free in a long time and continuous monitoring. Partial Least Squares (PLS) method is often used in ultraviolet absorption spectrum modeling, which is difficult to optimize and has low generalization ability. The neural network can compel any no-linear function by any precision, which has high generalization ability in the modeling. A neural network model is established in the in-situ nitrate sensor to measure the nitrate concentration in seawater in which the nitrate concentration range is 30 similar to 750 mu g . L-1. Double-hidden layer neural network model is determined to adopt by contrasting performance of single-hidden layer and double-hidden layer to measure nitrate concentration, the input layer is absorption spectrum from 200 to 275 nm, the output layer is nitrate concentration, and sigmoid function is used as the activation function. Gradient descent method is used to update weighting parameters for the neural network of each layer, after 55 000 times iteration, network training is conducted based on the learning rate of 0. 26. After validation for the blind test of the model through 8-group randomized validation data, the nitrate concentration using double-hidden layer neural network model is higher in linear correlation to its actual concentration (R-2 = 0. 997) in which the Root Mean Squared Error is 10. 864, average absolute error is 8. 442 mu g . L-1, average the relative error is 2. 8%. Compared with single-hidden layer neural network model, the double-hidden layer neural network model has higher accuracy in which the average relative error is reduced by 4. 92% , the Root Mean Squared Error of PLS is 4. 58% using the same spectral data, while the mean relative error is 11. 470. The result shows that the neural network model is much better than the Partial Least Squares model under certain conditions. It verifies the superiority of the neural network model applied to the nitrate concentration measurement by ultraviolet absorption spectrometry. The application test was carried out on the "Environmental Monitoring 01" monitoring vessel of the Ministry of Natural Resources, the measurement results are basically identical with the laboratory method in 11 stations, which is further proved from the reliability and practicality.
收录类别SCI
语种英语
关键词[WOS]ULTRAVIOLET; COMPUTATION
研究领域[WOS]Spectroscopy
WOS记录号WOS:000584498300037
引用统计
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/28294
专题中科院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室
中科院海岸带环境过程与生态修复重点实验室
通讯作者Feng Wei-wei作者单位1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Shangdong Prov Yantai Ecoenvironm Monitoring Ctr, Yantai 264000, Peoples R China
推荐引用方式
GB/T 7714Hou Yao-bin,Feng Wei-wei,Cai Zong-qi,et al. Nitrate Measurement in the Ocean Based on Neural Network Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2020,40(10):3211-3216.
APAHou Yao-bin,Feng Wei-wei,Cai Zong-qi,Wang Huan-qing,&Liu Zeng-dong.(2020).Nitrate Measurement in the Ocean Based on Neural Network Model.SPECTROSCOPY AND SPECTRAL ANALYSIS,40(10),3211-3216.
MLAHou Yao-bin,et al."Nitrate Measurement in the Ocean Based on Neural Network Model".SPECTROSCOPY AND SPECTRAL ANALYSIS 40.10(2020):3211-3216.
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