Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition

Wang, Zibo1,2,3; Lu, Wenxi1,2,3; Chang, Zhenbo4

2024-10-10

10.1016/j.scitotenv.2024.174374

SCIENCE OF THE TOTAL ENVIRONMENT   影响因子和分区

0048-9697

1879-1026

Groundwater pollution source recognition (GPSR) is a prerequisite for subsequent pollution remediation and risk assessment work. The actual observed data are the most important known condition in GPSR, but the observed data can be contaminated with noise in real cases. This may directly affect the recognition results. Therefore, denoising is important. However, in different practical situations, the noise attribute (e.g., noise level) and observed data attribute (e.g., observed frequency) may be different. Therefore, it is necessary to study the applicability of denoising. Current studies have two deficiencies. First, when dealing with complex nonlinear and non-stationary situations, the effect of previous denoising methods needs to be improved. Second, previous attempts to analyze the applicability of denoising in GPSR have not been comprehensive enough because they only consider the influence of the noise attribute, while overlooking the observed data attribute. To resolve these issues, this study adopted the variational mode decomposition (VMD) to perform denoising on the noisy observed data in GPSR for the first time. It further explored the influence of different factors on the denoising effect. The tests were conducted under 12 different scenarios. Then, we expanded the study to include not only the noise attribute (noise level) but also the observed data attribute (observed frequency), thus providing a more comprehensive analysis of the applicability of denoising in GPSR. Additionally, we used a new heuristic optimization algorithm, the collective decision optimization algorithm, to improve the recognition accuracy. Four representative scenarios were adopted to test the ideas. The results showed that the VMD performed well under various scenarios, and the denoising effect diminished as the noise level increased and the observed frequency decreased. The denoising was more effective for GPSR with high noise levels and multiple observed frequencies. The collective decision optimization algorithm had a good inversion accuracy and strong robustness.

Inverse problem Noise level Observed frequency Variational mode decomposition Collective decision optimization algorithm

SCI ; EI

英语

其他

National Natural Science Foundation of China[42272283]

Environmental Sciences & Ecology

Environmental Sciences

WOS:001265931500001

ELSEVIER

20242716646375

Groundwater ; Groundwater pollution ; Inverse problems ; Optimization ; Risk assessment

Groundwater:444.2 ; Water Pollution Sources:453.1 ; Information Theory and Signal Processing:716.1 ; Accidents and Accident Prevention:914.1 ; Optimization Techniques:921.5

ENVIRONMENT/ECOLOGY

Web of Science

1.Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China
2.Jilin Univ, Jilin Prov Key Lab Water Resources & Environm, Changchun 130021, Peoples R China
3.Jilin Univ, Coll New Energy & Environm, Changchun 130021, Peoples R China
4.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Peoples R China

Wang, Zibo,Lu, Wenxi,Chang, Zhenbo. Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,946.

Wang, Zibo,Lu, Wenxi,&Chang, Zhenbo.(2024).Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition.SCIENCE OF THE TOTAL ENVIRONMENT,946.

Wang, Zibo,et al."Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition".SCIENCE OF THE TOTAL ENVIRONMENT 946(2024).