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| Nonlinear Full-Spectrum Quantitative Analysis Algorithm of Complex Water Based on IERT |
| LIU Jia-cheng1, 2, HU Bing-liang1, YU Tao1*, WANG Xue-ji1, DU Jian1, LIU Hong1, LIU Xiao1, HUANG Qi-xing3 |
1. Key Laboratory of Spectral Imaging Technique, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Shenzhen Yantian Port Group Co., Ltd., Shenzhen 518081, China |
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Abstract Water is a finite resource, essential for agriculture, industry and even human existence. A good water environment is an important guarantee for sustainable development. The scientific monitoring of water quality information is the basis for optimal allocation and efficient use of water resources. The United Nations Environment Program (UNEP) and the World Health Organization (WHO) pointed out that national water quality monitoring networks in developing countries should be strengthened, including improving analytical capabilities and data quality assurance. As an emerging water quality analysis method, spectral method has the characteristics of “fast response, synchronization of multiple parameters, environmental protection and pollution-free” compared with traditional chemical water quality monitoring methods. The traditional single-band, multi-band linear model, relies on the absorption characteristics of water at specific bands, and it cannot be used for multi-component mixed solutions and has poor universality. Therefore, this paper proposes a non-linear full-spectrum quantitative analysis algorithm based on IERT. The concentration prediction model suitable for multi-component mixed solution is established to use full spectrum information to predict concentration information. We use the COD, BOD5, TOC multi-component mixed solution and NO3-N, turbidity, chroma multi-component mixed solution configured in the laboratory as the experimental sample, use the spectrometer to collect the spectral curve of the sample, and conduct the concentration prediction experiment through the full spectrum data. The experimental results show that for COD, BOD5, TOC multi-component mixed solutions, the determination coefficients （R2） of this algorithm for the three components are 0.999 3, 0.991 4 and 0.999 3. The root means square error (RMSE) is 0.024 4, 0.057 7 and 0.000 4. For the multi-component mixed solution of NO3-N, turbidity, and colority, the coefficient of determination （R2） is 0.983 4, 0.868 4 and 0.981 0. The root means square error (RMSE) is 0.100 5, 0.326 4 and 0.120 2. By comparing the experimental results of this algorithm with partial least squares (PLS), support vector regression (SVR), decision tree (DT), and extreme random tree (ERT) for the same set of data, the results show that in the experiment of mixed solution, this algorithm is the best alternative to the coefficient of determination （R2） of each component.The root means square error (RMSE) has been greatly reduced compared with other comparison algorithms. This algorithm can use spectral information to analyze the multi-component mixed solution quantitatively. It can effectively improve the concentration prediction accuracy and reduce the root-mean-square error of the quantitative analysis in the case of equivalent calculation time. Moreover, this algorithm can provide a theoretical basis for spectral methods on water quality monitoring.
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Received: 2020-11-03
Accepted: 2021-02-27
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Corresponding Authors:
YU Tao
E-mail: yutao@opt.ac.cn
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