Application of the complementary ensemble empirical mode decomposition for the identification of simulation model parameters and groundwater contaminant sources

Identification of groundwater contaminant sources (GCSs) relies on actual observed data, and the observed data directly affects the accuracy of the identification results. However, the observed data inevitably contains noise due to accidental and systematic errors, and the identification results of GCSs based on the observed data containing noise are usually not reliable. This seriously restricts the rational design of contamination remediation plans and contamination risk assessment. To solve this problem, complementary ensemble empirical mode decomposition (CEEMD) was applied to the joint identification of simulation model parameters and GCSs in-formation in this study for the first time. The wavelet analysis and CEEMD methods were used to reduce the noise in observed concentrations respectively, and the noise reduction effect of the two methods was compared and analyzed. Then the simulated concentrations, observed concentrations and noise-reduced observed concentra-tions with the best noise reduction effect were then applied to identify simulation model parameters and GCSs information, respectively, after which the corresponding identification results were compared and analyzed. The results showed that CEEMD could reduce the noise contained in the observed concentrations more effectively and make it closer to the actual values when compared with the wavelet analysis method. The accuracy of identi-fication results based on the noise-reduced observed concentrations by was improved. As the noise contained in the observed concentrations increased, the noise reduction effect of the CEEMD decreased. Nevertheless, the accuracy of the identification results based on the noise-reduced observed concentrations was still higher than that of those based on the observed concentrations with the same intensity noise without denoising.