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Dam Deformation Data Preprocessing with Optimized Variational Mode Decomposition and Kernel Density Estimation

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Publicado en:Remote Sensing vol. 17, no. 4 (2025), p. 718
Autor principal: Chen, Siyu
Otros Autores: Lin, Chaoning, Gu, Yanchang, Sheng, Jinbao, Mohammad Amin Hariri-Ardebili
Publicado:
MDPI AG
Materias:
Dams
Outliers (statistics)
Structural engineering
Accuracy
Wavelet transforms
Structural safety
Deformation
Optimization
Signal processing
Deformation effects
Spectrum allocation
Localization
Density
Correlation coefficients
Correlation coefficient
Statistical analysis
Data analysis
Preprocessing
Lagrange multiplier
Fourier transforms
Noise reduction
Dam engineering
Algorithms
Decomposition
Methods
Satellite observation
Global navigation satellite system
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Resumen:Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation (KDE). The approach systematically processes data in three steps: First, VMD decomposes raw data into intrinsic mode functions without recursion. The parallel Jaya algorithm is used to adaptively optimize VMD parameters for improved decomposition. Second, the intrinsic mode functions containing outlier and noise characteristics are identified and separated using sample entropy and correlation coefficients. Finally, KDE thresholds are applied for outlier localization, while a data superposition method ensures effective denoising. Validation using simulated deformation data and Global Navigation Satellite Systems (GNSS)-based observed horizontal deformation from dam engineering demonstrates the method’s robustness in accurately identifying outliers and denoising data, achieving superior preprocessing performance.
ISSN:2072-4292
DOI:10.3390/rs17040718
Fuente:Advanced Technologies & Aerospace Database