Nitrate Measurement in the Ocean Based on Neural Network Model
HOU Yao-bin1, 2, FENG Wei-wei1, 2*, CAI Zong-qi1, WANG Huan-qing1, LIU Zeng-dong3
1. CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Shangdong Provincial Yantai Eco-environmental Monitoring Center, Yantai 264000, China
Abstract：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 30～750 μg·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 (R2=0.997) in which the Root Mean Squared Error is 10.864, average absolute error is 8.442 μg·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.
侯耀斌，冯巍巍，蔡宗岐，王焕卿，刘增东. 基于神经网络模型的海水硝酸盐测量方法研究[J]. 光谱学与光谱分析, 2020, 40(10): 3211-3216.
HOU Yao-bin, FENG Wei-wei, CAI Zong-qi, WANG Huan-qing, LIU Zeng-dong. Nitrate Measurement in the Ocean Based on Neural Network Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(10): 3211-3216.
[1] Meyer D, Prien R, et al. Frontiers in Marine Science, 2018, 5: 431.
[2] Singh P, Singh M K, Beg Y R, et al. Talanta, 2019, 191(3): 364.
[3] FENG Wei-wei, LI Ling-wei, LI Wei-ran, et al(冯巍巍, 李玲伟, 李未然, 等). Acta Phonica Sinica(光子学报), 2012, 41(8): 883.
[4] Johnson K S, Coletti L J. Deep Sea Research Part I: Oceanographic Research Papers, 2002, 49(7): 1291.
[5] Sakamoto C M, Johnson K S, Coletti L J. Limnology and Oceanography-Methods, 2009, 7(1): 132.
[6] Johnson K S, Coletti L J, et al. Journal of Atmospheric and Oceanic Technology, 2013, 30(8): 1854.
[7] Sakamoto C M, Johnson K S, Coletti L J, et al. Limnology and Oceanography: Methods, 2017, 15(10): 897.
[8] CHENG Chang-kuo, SONG Jia-ju, DU Jun-lan, et al(程长阔, 宋家驹, 杜军兰, 等). Transducer and Microsystem Technologies(传感器与微系统), 2017, 36(12): 75.
[9] CHEN Shan-lin, HUANG Chun-hui(陈珊琳, 黄春晖). Chinese Journal of Quantum Electronics(量子电子学报)，2017, 34(4): 465.
[10] ZHANG Zheng-bin(张正斌). Marine Chemistry(海洋化学). Qingdao: China Ocean University Press(青岛：中国海洋大学出版社)，2004. 121.
[11] Shan L, Wang Y X, Li J L, et al. Surface & Coatings Technology, 2013, 226: 40.
[12] WANG Zhi-fang, WANG Shu- tao, WANG Gui-chuan(王志芳，王书涛，王贵川). Acta Photonica Sinica(光子学报), 2019, 48(4): 0412004.
[13] Ren C, An N, Wang J Z, et al. Knowledge-Based Systems, 2014, 56(1): 226.
