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

Farhad Alizadeh, Kiyoumars Roushangar
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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.

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利用 EMD、VMD、EWT 和 EFD 进行每日湖泊水位时间序列频谱分析
查看 largeDownload 幻灯片查看 largeDownload 幻灯片 关闭模态本研究使用频谱分析工具对乌尔米耶湖水位(ULL)的日变化动态进行了研究,以发现乌尔米耶湖水位序列的波动模式。因此,本研究使用了经验模式分解 (EMD)、变异模式分解 (VMD)、经验小波变换 (EWT) 和经验傅里叶分解 (EFD) 来分析 ULL 信号。将 ULL 序列分解为子序列,并使用优化结果。所有方法的结论都是,ULL 序列呈陡峭的下降趋势。对信号进行重构后推断,EFD 无法正确估计 ULL 序列,其均方根误差(RMSE)= 12.26。与 EFD 不同,根据均方根误差和误差分析,其他方法的信号构建效果更好。模式混合问题是验证信号分析方法能力的最后一步。根据功率谱密度(PSD),可以看出 EMD 存在模式混合问题,在信号分解方面存在局限性,而 VMD 和 EWT 则不存在这些问题。结果表明,本研究存在一些局限性。总体而言,在从 ULL 信号中分解和提取特征时,VMD 在均方根误差、误差分析、重构、模式混合问题和 PSD 分析方面表现更好。
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