Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity

Empirical mode decomposition (EMD) is a commonly used method in environmental science to study environmental variability in specific time period. Empirical mode decomposition is a sifting process that aims to decompose non-stationary and non-linear data into their embedded modes based on the local e...

Full description

Saved in:
Bibliographic Details
Published in:Environmental modeling & assessment 2019-08, Vol.24 (4), p.437-456
Main Authors: Z. Bahri, F. M., Sharples, J. J.
Format: Article
Language:English
Subjects:
Applications of Mathematics
Decision making
Decomposition
Earth and Environmental Science
Environment
Environmental assessment
Environmental impact
Environmental science
Interpolation
Math. Appl. in Environmental Science
Mathematical Modeling and Industrial Mathematics
Methods
Operations Research/Decision Theory
Sensitivity analysis
Sifting
Smoothing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Empirical mode decomposition (EMD) is a commonly used method in environmental science to study environmental variability in specific time period. Empirical mode decomposition is a sifting process that aims to decompose non-stationary and non-linear data into their embedded modes based on the local extrema. The local extrema are connected by interpolation. The results of EMD strongly impact the environmental assessment and decision making. In this paper, the sensitivity of EMD to different interpolation methods, linear, cubic, and smoothing-spline, is examined. A range of non-stationary data, including linear, quadratic, Gaussian, and logarithmic trends as well as noise, is used to investigate the method’s sensitivity to different types of non-stationarity. The EMD method is found to be sensitive to the type of non-stationarity of the input data, and to the interpolation method in recovering low-frequency signals. Smoothing-spline interpolation gave overall the best. The accuracy of the method is also limited by the type of non-stationarity: if the data have an abrupt change in amplitude or a large change in the variance, the EMD method cannot sift correctly.
ISSN:1420-2026
1573-2967
DOI:10.1007/s10666-019-9654-6