Chun-ling WANG, Kai-yuan SHI, Xing MING, Mao-qin CONG, Xin-yue LIU, Wen-ji GUO. A Comparative Study of the COD Hyperspectral Inversion Models in Water Based on the Maching Learning[J]. Spectroscopy and Spectral Analysis, 2022, 42(8): 2353
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 8, 2353 (2022)
Fig. 1. The original spectral reflectance curve of water samples
Fig. 2. Spectral profiles of water samples after preprocess
(a): SG smoothing; (b): MSC; (c): SG smoothing and MSC
(a): SG smoothing; (b): MSC; (c): SG smoothing and MSC
Fig. 3. Relationship between the number of decision trees and model MSE on training sample
(a): Random forest; (b): Adaboost; (c): XGBoost
(a): Random forest; (b): Adaboost; (c): XGBoost
Fig. 4. Sccetterplots of XGBoost inversion model based on different preprocessing methods
(a): Original data; (b): MSC; (c): SG smoothing; (d): SG smoothing and MSC
(a): Original data; (b): MSC; (c): SG smoothing; (d): SG smoothing and MSC
Fig. 5. The variancecontribution rate of the first ten principal components about PCA
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Table 1. Results of chemical oxgen demand (COD) statictical value of samples
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Table 2. The results of machine learning model based on orginal data
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Table 3. The results of machine learning model based on data processed by SG smoothing
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Table 4. The results of machine learning model based on data processed by MSC
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Table 5. The results of machine learning model based on data processed by SG smoothing and MSC
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Table 6. The result of the XGBoost model built based on the PCA method
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