COD content estimation of multi-feature fusion  based on water transmitted spectrum

WANG Cailing; ZHANG Yuchun; WANG Jingyi

doi:10.5768/JAO202142.0302006

Journal of Applied Optics > 2021 > 42(3): 488-493. > DOI: 10.5768/JAO202142.0302006

WANG Cailing, ZHANG Yuchun, WANG Jingyi. COD content estimation of multi-feature fusion based on water transmitted spectrum[J]. Journal of Applied Optics, 2021, 42(3): 488-493. DOI: 10.5768/JAO202142.0302006

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COD content estimation of multi-feature fusion based on water transmitted spectrum

School of Computer Science, Xi’an Shiyou University, Xi’an 710065, China

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Received Date: January 13, 2021
Revised Date: March 08, 2021
Available Online: March 12, 2021

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Abstract

Abstract

The chemical oxygen demand (COD) is a parameter that can quickly detect the organic pollutants and can well reflect the degree of water pollution. A COD content estimation model of multi-feature fusion water based on transmitted spectrum measurement was proposed. The transmission hyperspectral method collected 100 groups of COD water spectral information, preprocessed the spectral data, selected the characteristic band, analyzed the influence of different preprocessing methods on the model accuracy, carried out the feature fusion, and established the BP neural network model. By comparing the accuracy of the model, the optimal model was selected to detect the water COD content. The results show that the decision coefficient R² of BP neural network model based on multi-feature fusion is 0.991 64, and the root-mean-square error (RMSE) is 0.030 9. Compared with the partial least square method, the model has higher goodness of fit and higher accuracy.The multi-feature fusion based BP neural network hyperspectral detection method can realize the detection of COD content in water, and can be applied to the detection of other components in water.

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[1]	赵友全, 王慧敏, 刘子毓, 等. 基于紫外光谱法的水质化学需氧量在线检测技术[J]. 仪器仪表学报,2010,31(9):1927-1932. ZHAO Youquan, WANG Huimin, LIU Ziyu, et al. Novel method for on-line water COD determination using UV spectrum technology[J]. Journal of instrumentation,2010,31(9):1927-1932.
[2]	罗国兵. 水体化学需氧量的检测方法[J]. 岩矿测试,2013,32(6):860-874. LUO Guobing. A review on detection methods of chemical oxygen demand in water bodies[J]. Rock and Mineral Testing,2013,32(6):860-874.
[3]	KIMBERLY R, KHALID A. Application of a partial least-squares regression model to retrieve chlorophyll-a concentrations in coastal waters using hyper-spectral data[J]. Ocean Science Journal,2016,51(2):209-221. doi: 10.1007/s12601-016-0018-8
[4]	KHALID A, JOSEPH D. Multivariate approach for chlorophyll-a and suspended matter retrievals in Case II type waters using hyperspectral data[J]. Hydrological Sciences Journal,2016,61(1):200-213. doi: 10.1080/02626667.2014.964242
[5]	曹引, 冶运涛, 赵红莉, 等. 基于离散粒子群和偏最小二乘的水源地浊度高光谱反演[J]. 农业机械学报,2018,49(1):173-182. CAO Yin, YE Yuntao, ZHAO Hongli, et al. Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares[J]. Journal of agricultural machinery,2018,49(1):173-182.
[6]	张贤龙. 基于激光诱导击穿光谱和高光谱技术的水质指标定量研究[D]. 乌鲁木齐: 新疆大学, 2019. ZHANG Xianlong. Quantitative analysis on water quality indexs based on laser induced breakdown spectroscopy and hyperspectral technology[D]. Urumqi: Xinjiang University, 2019.
[7]	蔡建楠, 刘海龙, 姜波, 等. 基于GA-PLS算法的河网水体化学需氧量高光谱反演[J]. 灌溉排水学报,2020,39(9):126-131. CAI Jiannan, LIU Hailong, JIANG Bo, et al. Using hyperspectral imagery and GA-PLS algorithm to estimate chemical oxygen demand concentration of water in river network[J]. Journal of irrigation and drainage,2020,39(9):126-131.
[8]	尹光志, 李铭辉, 李文璞, 等. 基于改进BP神经网络的煤体瓦斯渗透率预测模型[J]. 煤炭学报,2013,38(7):1179-1184. YIN Guangzhi, LI Minghui, LI Wenpu, et al. Model of coal gas permeability prediction based on improved BP neural network[J]. Acta Coalae Sinica,2013,38(7):1179-1184.
[9]	朱亚东, 何鸿举, 王魏, 等. 高光谱成像技术结合线性回归算法快速预测鸡肉掺假牛肉[J]. 食品工业科技,2020,41(4):184-189. ZHU Yadong, HE Hongju, WANG Wei, et al. Quick detection of beef adulteration using hyperspectral imaging technology combined with linear regression algorithm[J]. Food Industry Science and Technology,2020,41(4):184-189.
[10]	李尚科, 杜国荣, 李跑, 等. 基于近红外光谱技术和优化预处理方法的豆浆粉无损鉴别研究[J]. 食品研究与开发,2020,41(17):144-150. LI Shangke, DU Guorong, LI Pao, et al. Nondestructive identification of soybean milk powder based on near infrared spectroscopy and optimized pretreatment method[J]. Food Research and Development,2020,41(17):144-150.
[11]	陶培峰, 王建华, 李志忠, 等. 基于高光谱的土壤养分含量反演模型研究[J]. 地质与资源,2020,29(1):68-75+84. TAO Peifeng, WANG Jianhua, LI Zhizhong, et al. Reserch of soil nutrient content inversion model based on hyperspectral data[J]. Geology and Resources,2020,29(1):68-75+84.
[12]	李尚科, 李跑, 杜国荣, 等. 基于近红外光谱技术和优化预处理方法的不同品牌燕麦无损鉴别分析[J]. 食品安全质量检测学报,2019,10(24):8204-8210. LI Shangke, LI Pao, DU Guorong, et al. Non-destructive identification of different brands of oats based on near-infrared spectroscopy and optimized pretreatment methods[J]. Journal of Food Safety & Quality,2019,10(24):8204-8210.
[13]	蔡志锋, 袁宝华, 刘广海. 基于深度特征融合的图像分类算法的研究[J]. 计算机应用与软件,2020,37(10):175-179+243. CAI Zhifeng, YUAN Baohua, LIU Guanghai. Image classification method based on deep feature fusion[J]. Computer Applications and Software,2020,37(10):175-179+243.
[14]	周延, 曹晖, 巨林仓. RMSECV曲线筛选光谱波段算法[J]. 光谱学与光谱分析,2011,31(2):492-495. ZHOU Yan, CAO Hui, JU Lincang. A spectral wavelength selection algorithm based on RMSECV curve[J]. Spectroscopy and Spectral Analysis,2011,31(2):492-495.
[15]	张娟, 原帅, 张骏. 基于小波变换-遗传算法-偏最小二乘的草莓糖度检测研究[J]. 分析科学学报,2020,36(1):111-115. ZHANG Juan, YUAN Shuai, ZHANG Jun. Detection of sugar degree in strawberry based on wavelet transform-genetic algorithm-partial least squares[J]. Journal of Analytical Science,2020,36(1):111-115.

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COD content estimation of multi-feature fusion based on water transmitted spectrum