Daily lake-level time series spectral analysis using EMD, VMD, EWT, and EFD

Abstract

This study investigates the dynamics of daily Urmia Lake level (ULL) changes using spectral analysis tools to discover fluctuating patterns in the ULL series. Therefore, in the present research, the empirical mode decomposition (EMD), variational mode decomposition (VMD), empirical wavelet transform (EWT), and empirical Fourier decomposition (EFD) were used to analyze the ULL signal. ULL series were decomposed into subseries, and the optimized outcome was used. All methods concluded that the ULL series has a steep downward trend. Signal reconstruction was performed, and it was inferred that EFD could not estimate the ULL series appropriately and had root-mean-square error (RMSE) = 12.26. Different from EFD, other methods performed better signal construction according to RMSE and error analysis. The mode-mixing issue was the last step in verifying the capabilities of signal-analyzing methods. Based on the power spectral density (PSD), it was seen that EMDs had mode-mixing problems and limitations in signal decomposition, whereas VMD and EWT did not have these issues. Results demonstrated that the present study has some limitations. Overall, it was concluded that VMD performed better in terms of RMSE, error analysis, reconstruction, mode-mixing problems, and PSD analysis while decomposing and extracting features from the ULL signal. HIGHLIGHTS Urmia Lake level (ULL) time series presented a steep downward trend, making it hard for signal processing methods to analyze accurately.; Variational mode decomposition (VMD) performed better than other methods in analyzing the ULL signal.; Empirical Fourier decomposition did not perform appropriately and was the weakest among other methods.; VMD had no mode-mixing issues contrary to empirical mode decompositions.;

Keywords

• efd
• emd
• ewt
• mode mixing
• urmia lake level
• vmd