IPSO-BPNN：一种结合粒子群优化的BP神经网络透射光谱水质亚硝酸盐含量定量化模型

doi:10.3964/j.issn.1000-0593(2024)11-3172-07

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Abstract Nitrite is a common water quality pollutant and is the main source of wastewater, fertilizer, and sewage treatment plants. The size of nitrite concentration in water quality is an important indicator to assess the health of water bodies. Still, the traditional method of nitrite concentration detection is complicated to operate. It easily interferes with the detection environment, which can not intuitively and accurately reflect the health of water quality. To explore a new way to assess the health of water bodies, this paper uses the IPSO-BPNN model to predict the concentration of nitrite transmission spectral data. Ten concentrations of nitrite standard solutions (0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, and 0.20 mg·L^-1) are first selected, and the ten concentrations of nitrite solutions are scanned at the same time intervals by using the OCEAN-HDX-XR micro spectrometer, The spectral transmittance of the spectral data is obtained by white board calibration to obtain spectral transmittance values for the spectral data. Two preprocessing methods, maximum-minimum normalization, and mean-centering, are used to unify the spectral data into uniform dimensions and centroids, making the spectral data comparable and interpretable among different samples. Due to the high dimensionality of the original spectral data, kernel principal component analysis is used for data dimensionality reduction, and six principal components representing 97.94% of the original data information are selected for the training of the IPSO-BPNN model. When predicting nitrite concentration, the original particle swarm optimization algorithm is improved by introducing adaptive learning factor and inertia weight updating formulae and particle population diversity guiding strategy, and learning rate adaptive formulae are introduced based on the BP neural network to improve the algorithm's performance. By comparing the change curves of function fitness values for iterations performed under different particles, 30 iterations using 100 particles are chosen to find the optimal weight and bias combinations. The results show that the coefficient of determination of the IPSO-BPNN prediction model is 0.983 760, the root-mean-square error is 0.007 320, and the average absolute error is 0.004 705, which is a better fit compared with the current Random Forest, Linear Regression, BP-ANN, PSO-BPNN, and PSO-SVR models that have better prediction performance and higher accuracy. Based on these results, a hyperspectral water quality nitrite concentration prediction method based on the IPSO-BPNN model is proposed, providing a new idea for assessing water body health.

Key words：Hyperspectral; Nitrite; IPSO-BPNN model; KPCA; Water quality testing

Received: 2023-07-26 Accepted: 2024-01-08

ZTFLH:

O657.3

Cite this article:

WANG Cai-ling,ZHANG Guo-hao. IPSO-BPNN: A Quantitative Model for Nitrite Content in Water Quality Using Transmissive Spectroscopy Combined With Improved Particle Swarm Optimization and Backpropagation Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3172-3178.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)11-3172-07 OR https://www.gpxygpfx.com/EN/Y2024/V44/I11/3172

[1] WANG Cai-ling, WANG Bo, JI Tong, et al(王彩玲, 王波, 纪童，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(7): 2181.
[2] WANG Wei, LIANG Ran, QI Yun, et al(汪伟, 梁然, 祁云，等). China Safety Science Journal(中国安全科学学报), 2023, 33(7): 127.
[3] LIU Dan, LIU Fang, XU Yan-ping(刘丹，刘方，许彦平). Acta Energiae Solaris Sinica(太阳能学报), 2022, 43(6): 94.
[4] LING Xiao, XU Lu-shuai, LIANG Rui, et al(凌晓，徐鲁帅，梁瑞，等). Journal of Safety Science and Technology(中国安全生产科学技术), 2019, 15(10): 63.
[5] HUANG Jin, LI Yun-fei, WANG Sheng-chun, et al(黄晋, 李云飞, 王圣淳，等). Electronics Optics & Control(电光与控制), 2024, 31(2): 41.
[6] FAN Yong, PEI Yong, YANG Guang-dong, et al(范勇, 裴勇, 杨广栋，等). Journal of Vibration and Shock(振动与冲击), 2022, 41(16): 194.
[7] ZHU Ya-dong, HE Hong-ju, WANG Wei, et al(朱亚东，何鸿举，王魏，等). Science and Technology of Food Industry(食品工业科技), 2020, 41(4): 184.
[8] XU Han-qiu, LI Chun-qiang, LIN Meng-jing(徐涵秋，李春强，林梦婧). Geomatics and Information Science of Wuhan University[武汉大学学报(信息科学版)], 2023, 48(4): 506.
[9] MA Zhan-fei, GONG Chuan-sheng, LI Ke-jian, et al(马占飞，巩传胜，李克见，等). Transducer and Microsystem Technologies(传感器与微系统)，2023，42(12)：151.
[10] XIE Jia, WU Jia-zhen, LI Yong-guo, et al(谢嘉, 吴家桢, 李永国，等). Application of Improved Particle Swarm Optimization Algorithm in Robot Arm Trajectory Planning(改进粒子群优化算法在机械臂轨迹规划中的应用). Mechanical Science and Technology for Aerospace Engineering(机械科学与技术), 2023.
[11] CHENG Mei-ying, QIAN Qian, NI Zhi-wei(程美英, 钱乾, 倪志伟). Control and Decision(控制与决策), 2024, 39(3): 728.
[12] LIU Tian-yu, WANG Zhu(刘天宇, 王翥). Journal of Xidian University(西安电子科技大学学报), 2021, 48(3): 106.