基于PCA透射光谱重构降噪的水体BOD含量模拟估算

doi:10.3964/j.issn.1000-0593(2025)02-0386-08

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Abstract Biochemical oxygen demand (BOD) is an important indicator that can directly reflect water bodies' degree of organic pollution. Real-time monitoring of water BOD is significant in water resource protection and water environment improvement. The traditional BOD measurement method will consume a lot of human and material resources, and the measurement cycle is long, which can not quickly reflect the changing conditions of the water body, and can not realize the timely and effective early warning of sudden water pollution events. With the wide application of machine learning in the field of water monitoring, to solve the problem of difficulty in obtaining the input variables of the machine learning model and the existence of missing values, we further combine the hyperspectral technology to realize the accurate and rapid estimation of the BOD content of the water body. The raw spectral data of ten BOD standard liquids with different concentrations were collected, and 100 sets of transmission spectral data were obtained by whiteboard correction. A noise reduction technique based on PCA transmission spectra reconstruction is proposed, which utilizes the PCA algorithm to extract the principal component eigenvectors of the original transmission spectra and then reconstructs the whole dataset by using the first part of the principal component eigenvectors whose cumulative variance contribution rate reaches a certain percentage. The first 2, 10, and 15 principal component feature vectors were used in the experiment to reconstruct the transmission spectral data and compared with the traditional noise reduction methods for spectral data. We combined the SVM model and BP neural network model to establish a model for estimating the BOD content of water bodies. The results showed that the BPNN model was superior to the SVM model regarding regression accuracy and degree of fit, and the noise reduction effect was more significant. The model using the first 2 feature vectors reconstructed for noise reduction did not fit as expected, probably due to the loss of information. The BPNN model with the first 10 feature vectors reconstructed for noise reduction performed the best with an RMSE of 0.040 6 and an R² of 0.980 3. The reconstruction of the first 15 feature vectors did not improve the noise reduction effect, probably because more than 10 feature vectors added redundant information. The experiments verified the feasibility of noise reduction using PCA reconstruction of transmission spectra and provided a new idea for estimating the BOD content of water bodies.

Key words：PCA; Hyperspectral; SVM; BP neural network; BOD estimation

Received: 2023-09-05 Accepted: 2024-05-31

ZTFLH:

O657.3

Corresponding Authors: WANG Hong-wei E-mail: wanghongwei@nwpu.edu.cn

Cite this article:

WANG Yi-ming,WANG Cai-ling,WANG Hong-wei. Simulated Estimation of BOD Content in Water Bodies Based on PCA Transmission Spectrum Reconstruction With Noise Reduction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 386-393.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)02-0386-08 OR https://www.gpxygpfx.com/EN/Y2025/V45/I02/386

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