Nitrate Measurement in the Ocean Based on Neural Network Model

doi:10.3964/j.issn.1000-0593(2020)10-3211-06

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室 > 近岸生态与环境实验室

	Nitrate Measurement in the Ocean Based on Neural Network Model
	Hou Yao-bin 2,3; Feng Wei-wei 2,3; Cai Zong-qi 3; Wang Huan-qing 3; Liu Zeng-dong 1
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2020-10-01
卷号	40 期号:10 页码:3211-3216
关键词	Ultraviolet Absorption spectroscopy Seawater Nitrate Neural network Spectral analysis
ISSN号	1000-0593
DOI	10.3964/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.
WOS关键词	ULTRAVIOLET ; COMPUTATION
WOS研究方向	Spectroscopy
语种	英语
WOS记录号	WOS:000584498300037
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/28293]
专题	烟台海岸带研究所_近岸生态与环境实验室烟台海岸带研究所_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.Shangdong Prov Yantai Ecoenvironm Monitoring Ctr, Yantai 264000, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Hou 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.
APA	Hou 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.
MLA	Hou Yao-bin,et al."Nitrate Measurement in the Ocean Based on Neural Network Model".SPECTROSCOPY AND SPECTRAL ANALYSIS 40.10(2020):3211-3216.