• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 7, 2113 (2022)
Ming-rui ZHOU1、*, Jiang-bei QU2、2;, Peng LI1、1; 2; *;, and Yi-liang HE1、1; 2;
Author Affiliations
  • 11. China-UK Low Carbon College, Shanghai Jiaotong University, Shanghai 201306, China
  • 22. School of Environmental Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China
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    DOI: 10.3964/j.issn.1000-0593(2022)07-2113-07 Cite this Article
    Ming-rui ZHOU, Jiang-bei QU, Peng LI, Yi-liang HE. The “Cluster-Regression” COD Prediction Model of Distributed Rural Sewage Based on Three-Dimensional Fluorescence Spectrum and Ultraviolet-Visible Absorption Spectrum[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2113 Copy Citation Text show less
    Flow chart of model design
    Fig. 1. Flow chart of model design
    The map of sampling sites in Changshu
    Fig. 2. The map of sampling sites in Changshu
    Three-dimensional map of the regional distribution of fluorescent substances
    Fig. 3. Three-dimensional map of the regional distribution of fluorescent substances
    Fluorescence spectra of water samples(a): Three-dimensional fluorescence spectra before removal of scattering;(b) Three-dimensional fluorescence spectra after scattering removal
    Fig. 4. Fluorescence spectra of water samples
    (a): Three-dimensional fluorescence spectra before removal of scattering;(b) Three-dimensional fluorescence spectra after scattering removal
    PARAFAC of water samples(a): Leverage analysis of the sample; (b): The split half analysis of emission wavelength with factors of 3; (c): The split half analysis of excitation wavelength with factors of 3
    Fig. 5. PARAFAC of water samples
    (a): Leverage analysis of the sample; (b): The split half analysis of emission wavelength with factors of 3; (c): The split half analysis of excitation wavelength with factors of 3
    FCM cluster analysis results(a): Selection of the optimal cluster number after using FRI; (b): Selection of the optimal cluster number after using PARAFAC;(c): Clustering results after using FRI; (d): Clustering results after using PARAFAC
    Fig. 6. FCM cluster analysis results
    (a): Selection of the optimal cluster number after using FRI; (b): Selection of the optimal cluster number after using PARAFAC;(c): Clustering results after using FRI; (d): Clustering results after using PARAFAC
    类别样品数目相同的样品数重合率/%
    PARAFACFRIPARAFACFRIPARAFACFRI
    第一类365727277547.40
    第二类595510055.60
    第三类2934242482.8070.60
    第四类2700000
    Table 1. Specific clustering results
    类别回归预测
    未分类FRIPARAFAC未分类FRIPARAFAC
    R2RMSER2RMSER2RMSER2RMSER2RMSER2RMSE
    第一类--0.8617.1690.92310.315--0.85217.6180.95414.252
    第二类--0.79626.1770.9991.122--0.78738.484--
    第三类--0.81913.190.91810.962--0.81914.7610.9025.937
    第四类----0.92113.623----0.86319.025
    均值0.65727.3350.82518.8450.9409.0060.63227.8570.81923.6210.90613.071
    Table 2. The model fitting and prediction results
    Ming-rui ZHOU, Jiang-bei QU, Peng LI, Yi-liang HE. The “Cluster-Regression” COD Prediction Model of Distributed Rural Sewage Based on Three-Dimensional Fluorescence Spectrum and Ultraviolet-Visible Absorption Spectrum[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2113
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