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首页>《中国测试》期刊>本期导读>基于紫外可见吸收光谱的水质检测算法研究

基于紫外可见吸收光谱的水质检测算法研究

74    2019-05-28

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作者:林春伟, 郭永洪, 何金龙

作者单位:中国计量大学, 浙江 杭州 310018


关键词:水质检测;光谱分析;支持向量机;改进差分进化算法;小波变换;主成分分析


摘要:

为实时有效地检测地表水中硝酸根离子和亚硝酸根离子的变化过程,提出一种基于紫外可见吸收光谱的水质检测算法。针对水质光谱数据受到干扰易出现波动误差的问题,采用小波变换对其进行分解以滤除高频噪声,并通过主成分分析对数据特征进行降维以防止模型复杂度较高导致过拟合。水质光谱数据经预处理后采用支持向量机对其进行建模,通过非线性自适应调整变异收缩因子对差分进化算法进行改进,并采用改进差分进化算法对水质预测模型进行参数优化。通过与采用其他常用算法所建模型进行对比分析,实验结果表明:基于该算法所建的硝酸根离子和亚硝酸根离子模型具有更高的预测精度,且其能够以更快的收敛速度使模型达到全局最优。


Research on water quality detection algorithm based on UV-vis absorption spectral
LIN Chunwei, GUO Yonghong, HE Jinlong
China Jiliang University, Hangzhou 310018, China
Abstract: In order to detect the change process of nitrate ions and nitrite ions in surface water effectively and in real time, a water quality detection algorithm based on UV-vis absorption spectra was proposed. Aiming at the problem that water quality spectral data are disturbed and easy to appear wave error, wavelet transform is used to decompose the water quality spectral data to filter out the high frequency noise, and the principal component analysis is used to reduce the dimension of data features to prevent the over-fitting caused by high complexity of the model. The water quality spectral data are modeled by support vector machine after pretreatment, and the differential evolution algorithm is improved by nonlinear adaptive adjustment of variation shrinkage factor, and the parameters of the water quality prediction model are optimized by the improved differential evolution algorithm. By comparing with the models built by other common algorithms, the experimental results show that this algorithm can make the nitrate ion and nitrite ion models have higher prediction accuracy, and it can make the model achieve global optimization with faster convergence speed.
Keywords: water quality detection;spectral analysis;support vector machine;improved differential evolution algorithm;wavelet transform;principal component analysis
2019, 45(5):79-84  收稿日期: 2018-09-06;收到修改稿日期: 2018-11-09
基金项目: 浙江省自然科学基金(Y14F010075)
作者简介: 林春伟(1994-),男,河南鹤壁市人,硕士研究生,专业方向为检测技术
参考文献
[1] ADU-MANU K S, TAPPARELLO C, HEINZELMAN W, et al. Water quality monitoring using wireless sensor networks:current trends and future research directions[J]. ACM Transactions on Sensor Networks, 2017, 13(1):1-41
[2] STOREY M V, VAN DER GAAG B, BURNS B P. Advances in on-line drinking water quality monitoring and early warning systems[J]. Water Research, 2011, 45(2):741-747
[3] JIAO L, DONG D, ZHENG W. Determination of thiophanate-methyl using UV absorption spectra based on multiple linear regression[J]. Optik, 2014, 125(1):183-185
[4] SKOU P B, BERG T A, AUNSBJERG S D, et al. Monitoring process water quality using near infrared spectroscopy and partial least squares regression with prediction uncertainty estimation[J]. Applied Spectroscopy, 2017, 71(3):410-421
[5] OZBALCI B, BOYACI IH, TOPCU A, et al. Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks[J]. Food Chemistry, 2013, 136(3-4):1444-1452
[6] DEVOS O, RUCKEBUSCH C, DURAND A, et al. Support vector machines (SVM) in near infrared (NIR) spectroscopy:Focus on parameters optimization and model interpretation[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 96(1):27-33
[7] 曾甜玲, 温志渝, 温中泉, 等. 基于紫外光谱分析的水质监测技术研究进展[J]. 光谱学与光谱分析, 2013, 33(4):1098-1103
[8] HUO A D, ZHANG J, QIAO C L, et al. Multispectral remote sensing inversion for city landscape water eutrophication based on genetic algorithm-support vector machine[J]. Water Quality Research Journal of Canada, 2014, 49(3):285-293
[9] WANG X, LÜ J K, XIE D T. A hybrid approach of support vector machine with particle swarm optimization for water quality prediction[J]. International Conference on Computer Science and Education, 2010, 31(7):1158-1163
[10] CIVICIOGLU P, BESDOK E. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms[J]. Artificial Intelligence Review, 2013, 39(4):315-346
[11] 汤斌, 魏彪, 毛本将, 等. 紫外-可见吸收光谱法水质检测系统的噪声分析与处理研究[J]. 激光与光电子学进展, 2014, 51(4):1-7
[12] 陈扬, 张太宁, 郭澎, 等. 基于主成分分析的复杂光谱定量分析方法的研究[J]. 光学学报, 2009, 29(5):1285-1291
[13] CHANG C C, LIN C J. LIBSVM:A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):1-27
[14] 曹飞凤, 许月萍. 基于改进差分进化算法的水文模型参数多目标优选研究[J]. 系统工程理论与实践, 2014, 34(12):3268-3273