基于IPLS-GAN-SVM混合算法的水体COD光谱模型研究
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桂林理工大学信息科学与工程学院

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TP183;O657

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国家自然科学基金(62166012);广西嵌入式技术与智能系统重点实验室基金(202304);广西研究生教育创新计划项目(YCSW2022319)


Research On the Chemical Oxygen Demand Spectral Model in Water Based On IPLS-GAN-SVM Hybrid Algorithm
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College of Information Science and Engineering, Guilin University of Technology.

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    摘要:

    光谱共线性和有限的光谱数据集是影响化学需氧量反演的两个主要问题,针对第一个问题,为选取最优建模波段,首先采用标准正态变换、SG方法等6种预处理方法处理原始光谱并返回处理结果,其次使用IPLS建模,具体为将190nm~350nm光谱区域划分为10个等宽的子区间,并在每个区间上对预处理后的数据和原始数据进行偏最小二乘回归,以建立多个局部回归模型。结果表明:模型在第7个波段(238~253nm)建模效果最好,使用原始数据建模得到的MSE,MAE和R2score的值分别为0.2172,0.0278,0.9942,SG方法的预处理效果好于其他5种预处理方法,相比于直接对原始数据建模,MSE,MAE下降至 1.4727,1.0318,R2score提升至0.9944。针对数据集较小的问题,基于原始的数据特征,训练3种GAN网络以进行数据扩充并使用SVM建模。实验结果表明:相比于原始数据,模型的MSE和MAE有明显下降,模型的ACC和R2score有明显的上升,其中3个模型的ACC依次提升了2.88%,11.53%和11.53%,R2score依次提升了18.07%,17.40%和18.74%。基于GAN网络的数据增强方法在光谱分析技术领域具有重要的研究意义和应用价值。

    Abstract:

    Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand(COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Transform, Savitzky-Golay Smooth Filtering(SGSF) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares(IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error(MSE), Mean Absolute Error(MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SGSF is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generate Adversarial Networks(GAN) for data augmentation, three datasets are obtained for Support Vector Machine(SVM) modeling. The results indicate that, compared to the original dataset, the SVM"s MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%. The data enhancement method based on GAN network has important research significance and application value in the field of spectral analysis.

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陆绮荣,邹健,叶颖雅,等. 基于IPLS-GAN-SVM混合算法的水体COD光谱模型研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-08
  • 最后修改日期:2024-03-19
  • 录用日期:2024-03-21
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