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| Estimation of COD content by transmission spectroscopy combined with PCA |
| Received:July 12, 2023 Revised:January 27, 2024 |
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| DOI：10.3969/j.issn.2095-1035.2024.04.005 |
| KeyWord:transmitted spectrum method measurement; COD content prediction; PCA; Gaussian Process Regression; BP neural network |
| Author | Institution |
| WANG Cailing |
西安石油大学计算机学院 |
| Wei xinxin |
西安石油大学计算机学院 |
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| Abstract: |
| COD is an important indicator of organic pollution in water, and the higher the COD, the more serious the degree of water pollution. To solve the traditional method of measuring COD is time-consuming, not conducive to rapid, real-time access to COD information in the water body and other issues. In this paper, an improved model for the estimation of COD content in water bodies on the basis of transmission spectroscopy measurement combined with principal component analysis (PCA) is proposed. Specifically, 100 groups of COD water body spectral information were collected, and three different hyperspectral data preprocessing methods were used to preprocess the spectral data, and Gaussian Process Regression (GPR) and BP neural network models were constructed based on different preprocessing methods to analyze the effects of different preprocessing methods on the accuracy of the models. In order to analyze effects of different preprocessing methods on model accuracy, GPR and BP neural networks have been constructed based on different preprocessing methods. Compared with GPR model and BP neural network model constructed from original spectrum data, it was found that after data pre-processing, there was a significant improvement in model accuracy, and after further dimension reduction of pre-processing data combined with PCA, there was a further improvement in model accuracy. Among them, the R^2 of the improved BP neural network model based on standard normal variable transformed features combined with PCA is as high as 0.9940, and the RMSE is 0.022540. This proves that the dimensionality reduction of the preprocessed spectral data based on the PCA data dimensionality reduction method helps to remove the redundant information in the spectral data and extract the feature information, and optimizes the COD content estimation model, thereby solving the problems of the traditional COD measurement methods. Thus, a new idea for the solution of the problems that exist in the traditional method of COD measurement is proposed. |
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