Research on denoising of joint detection signal of water quality with multi-parameter based on IEEMD

Abstract

An improved ensemble empirical mode decomposition (IEEMD) is suggested to process water quality spectral signals in order to address the issue that noise interference makes it difficult to extract and evaluate water quality spectral signals. This algorithm effectively solves the problems of modal mixing, poor reconstruction accuracy in the empirical mode decomposition (EMD), and a large amount of calculation in the ensemble empirical mode decomposition (EEMD). Based on EEMD, IEEMD firstly preprocesses the original water quality spectral signals, then performs Savitzky-Golay (S-G) smoothing on the decomposed effective intrinsic mode function (IMF) components, and finally reconstructs them to obtain the denoised signals. Water sample data at different concentrations can be accurately analyzed based on the noise-reduced spectral signals. In this paper, three water quality parameters are used as research objects: benzene (C₆H₆), benzo(b)fluoranthene (C₂₀H₁₂), and chemical oxygen demand (COD). The original water quality multi-parameter (C₆H₆, C₂₀H₁₂, COD) spectral signals were subjected to denoising based on the IEEMD and the water quality multi-parameter joint detection technology. The signal-to-noise ratio (SNR) and the correlation coefficient (R ²) of the fitted curves obtained from the processing of the IEEMD were compared and analyzed with those obtained from the processing of the EMD and the EEMD. The experimental results show that the SNR of the spectral signals and the R ² of the fitting curve in three water quality parameters have been significantly improved. Therefore, the IEEMD effectively improves the phenomenon of modal mixing, reduces the amount of calculation, improves the reconstruction accuracy, and provides an important guarantee for the effective extraction of multi-parameter spectral signals of water quality.

References

[[1]] Li W, Wang L M, Cheng L, et al.. Study on a multiparameter method for the determination of water quality by sequential injection-continuous spectroscopy[J]. Spectroscopy and spectral analysis, 2021, 41(02): 612-617

[[2]] Huang X T, Chen Y X, Zhu Z B, et al.. Research progress on the detection of ascorbic acid based on nanomaterials spectroscopicanalysis[J]. Chinese journal of applied chemistry, 2021, 38(06): 637-650

[[3]] Xue P, He H, Wang H M. Error correction algorithm for optical measurement system based on radial basis function network[J]. Journal of optics, 2020, 40(02): 106-112 (in Chinese)

[[4]] Li Q B, Wei Y, Cui H X, et al.. A quantitative method for water quality TOC analysis based on UV-Vis spectroscopy[J]. Spectroscopy and spectral analysis, 2022, 42(02): 376-380

[[5]] YANG G F, DAI J C, LIU X J, et al. Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection[J]. Analytical methods, 2020, 12(2).

[[6]] VOUSOUGHI F D. Wavelet-based de-noising in groundwater quality and quantity prediction by an artificial neural network[J]. Water supply, 2023, 23(3).

[[7]] Huang N E, Shen Z, Long S R. The empiricalmode decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis[J]. Proceeding of the royal society of London, 1998, 454: 903-995,

[[8]] Wu Z H, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(1): 1-41,

[[9]] Zou N, Jin Y C, Guo C, et al.. Preprocessing of radar signals in wells based on EMD[J]. Journal of the university of electronic science and technology, 2022, 51(06): 875-883 (in Chinese)

[[10]] Liang Y, Liu T G, Liu K, et al.. Optimization method of gas detection based on variational modal decomposition algorithm[J]. Chinese journal of lasers, 2021, 48(07): 135-144, in Chinese)

[[11]] Cao L L, Li J, Peng Z, et al.. Research on rolling bearing fault diagnosis based on EEMD and fast spectral cliffness[J]. Journal of mechanical & electrical engineering, 2021, 38(10): 1311-1316 (in Chinese)

[[12]] XU J, ZHANG B Y, FU Q. Study on differential noise reduction technique in infrasound calibration[J]. Academic journal of engineering and technology science, 2022, 5(13).

[[13]] Zheng J D, Cheng J S, Yang Y. Improved EEMD algorithm and its application research[J]. Journal of vibration and shock, 2013, 32(21): 21-26+46 (in Chinese)

[[14]] Chen J, Kan D, Sun T H, et al.. Fault diagnosis method for rolling bearings based on SVD-VMD and SVM[J]. Journal of electronic measurement and instrumentation, 2022, 36(01): 220-226 (in Chinese)

[[15]] Zhang R, Zhang P, Zhao F. Research on blast shock wave denoising algorithm based on CEEMDAN-SG[J]. Foreign electronic measurement technology, 2022, 41(10): 119-125 (in Chinese)

[[16]] Li M, Zhao Y, Cui F P, et al.. Characterization of Raman spectral signals based on ensemble empirical modal decomposition[J]. Spectroscopy and spectral analysis, 2020, 40(01): 54-58 (in Chinese)

[[17]] Li W, Cai Y Q, Ma Y Y, et al.. Design of a dual-spectrum water quality multi-parameter integrated system based on embedded technology[J]. Instrument technique and sensor, 2022, 469(02): 101-106

[[18]] Wang X P, Liu Y F, Tian T, et al.. Research on the detection and simulation of water pollutants based on spectroscopic technology[J]. Journal of Huazhong University of Science and Technology (natural science edition), 2020, 48(03): 81-85

[[19]] Li J, Wang W B, Sheng L, et al.. A noise reduction method for microseismic signals applying bidirectional long and short term memory neural networks[J]. Oil geophysical prospecting, 2023, 58(02): 285-294