Data Synthesis Based on Empirical Mode Decomposition
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 7
Abstract
The purpose of this paper is to introduce an effective way to solve the problem of nonstationary data generation. Empirical mode decomposition (EMD) algorithms have been widely used in data diagnosis. A new EMD-based data synthesis method is proposed. The method utilizes the recombination of the intrinsic mode function (IMF) of the segmented data, as well as the characteristics of the residuals, to generate the data. This article takes the 100-year monthly temperature and rainfall data of Tainan, Taiwan, as an example. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is applied in the paper to verify the stationarity of the generated data. The EMD-based data synthesis effectively shows its applicability and provides new ideas for nonstationary data generation.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
In this study, the record of temperature and rainfall was purchased from the Central Weather Bureau, Taiwan (https://www.cwb.gov.tw).
Furthermore, the codes for EMD analysis are available on the National Central University Data Center (http://rcada.ncu.edu.tw/research1.htm).
Acknowledgments
This research was sponsored by the Ministry of Science and Technology in Taiwan (MOST 106-2625-M-019-001).
References
Agana, N. A., and A. Homaifar. 2018. “EMD-based predictive deep belief network for time series prediction: An application to drought forecasting.” Hydrology 5 (1): 18. https://doi.org/10.3390/hydrology5010018.
Bai, L., J. H. Xu, Z. S. Chen, W. H. Li, Z. H. Liu, B. F. Zhao, and Z. J. Wang. 2014. “The regional features of temperature variation trends over Xinjiang in China by the ensemble empirical mode decomposition method.” Int. J. Climatol. 35 (11): 3229–3237. https://doi.org/10.1002/joc.4202.
Central Weather Bureau of the Ministry of Transportation and Communications. 2005. “CWB observation data inquire system.” Accessed August 23, 2017. https://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp.
Dean, R. G., and R. A. Dalrymple. 1991. “Water wave mechanics for engineers and scientists.” In Advanced series on ocean engineering. Singapore: World Scientific. https://doi.org/10.1142/1232.
Ezer, T., and W. B. Corlett. 2012. “Is sea level rise accelerating in the Chesapeake Bay? A demonstration of a novel new approach for analyzing sea level data.” Geophys. Res. Lett. 39 (19): L19605. https://doi.org/10.1029/2012GL053435.
Haan, C. T. 1977. Statistical methods in hydrology, 275–286. Ames, IA: Iowa State Univ.
Hu, J., J. Lin, Y. Lin, and C. Gao. 2013. “EMD-KNN model for annual average rainfall forecasting.” J. Hydrol. Eng. 18 (11): 1450–1457. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000481.
Huang, N. E., and S. S. P. Shen. 2014. “Hilbert-Huang transform and its applications, interdisciplinary.” In Mathematical sciences. Singapore: World Scientific.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis.” In Proc., Royal Society A Mathematical, Physical and Engineering Sciences. 903–995. London: Royal Society. https://doi.org/10.1098/rspa.1998.0193.
Huang, S. Z., J. X. Chang, Q. Huang, and Y. T. Chen. 2014. “Monthly streamflow prediction using modified EMD-based support vector machine.” J. Hydrol. 511 (Apr): 764–775. https://doi.org/10.1016/j.jhydrol.2014.01.062.
Huang, Y., F. G. Schmitt, Z. Lu, and Y. Liu. 2009. “Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis.” J. Hydrol. 373 (1–2): 103–111. https://doi.org/10.1016/j.jhydrol.2009.04.015.
IPCC (Intergovernmental Panel on Climate Change). 2013. “Climate change 2013: The physical science basis.” In Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, edited by T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley. New York: Cambridge Universtiy Press.
Karthikeyan, L., and D. N. Kumar. 2013. “Predictability of nonstationary time series using wavelet and EMD based ARMA models.” J. Hydrol. 502 (Oct): 103–119. https://doi.org/10.1016/j.jhydrol.2013.08.030.
Kwon, H. H., U. Lall, and A. F. Khalil. 2007. “Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature.” Water Resour. Res. 43 (5): W05407. https://doi.org/10.1029/2006WR005258.
Lee, T., and T. B. M. J. Ouarda. 2011. “Prediction of climate nonstationary oscillation processes with empirical mode decomposition.” J. Geophys. Res. 116 (6): D06107. https://doi.org/10.1029/2010JD015142.
Lee, T., and T. B. M. J. Ouarda. 2012. “Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition.” J. Geophys. Res. 48 (2): W02514. https://doi.org/10.1029/2011WR010660.
Meng, E. H., S. Z. Huang, Q. Huang, W. Fang, L. Z. Wu, and L. Wang. 2019. “A robust method for non-stationary streamflow prediction based on improved EMD-SVM model.” J. Hydrol. 568 (Jan): 462–478. https://doi.org/10.1016/j.jhydrol.2018.11.015.
National Land Surveying and Mapping Center of the Ministry of the Interior. 2015. “County (city) administrative area boundary map resources of Taiwan.” Accessed August 23, 2017. https://data.gov.tw/dataset/7442.
Pegram, G. S., M. C. Peel, and T. A. McMahon. 2008. “Empirical mode decomposition using rational splines: An application to rainfall series.” In Proc., Royal Society a Mathematical. Physical and Engineering Sciences. 1483–1501. London: Royal Society. https://doi.org/10.1098/rspa.2007.0311.
Salas, J. D., and D. C. Boes. 1980. “Shifting level modeling of hydrologic series.” Adv. Water Res. 3 (2): 59–63. https://doi.org/10.1016/0309-1708(80)90028-7.
Salas, J. D., J. W. Delleur, V. Yevjevich, and W. L. Lane. 1985. Applied modeling of hydrologic time series. Littleton, CO: Water Resources Publications.
Sang, Y. F., Z. G. Wang, and C. M. Liu. 2014. “Comparison of the MK test and EMD method for trend identification in hydrological time series.” J. Hydrol. 510 (Mar): 293–298. https://doi.org/10.1016/j.jhydrol.2013.12.039.
Sveinsson, O. G. B., J. D. Salas, D. C. Boes, and R. A. Pielke. 2003. “Modeling the dynamics of long-term variability of hydroclimatic processes.” J. Hydrometeorol. 4 (3): 489–505. https://doi.org/10.1175/1525-7541(2003)004%3C0489:MTDOLV%3E2.0.CO;2.
Wu, Z. H., and N. E. Huang. 2009. “Ensemble empirical mode decomposition: A noise-assisted data analysis method.” Adv. Adapt. Data Anal. 1 (1): 1–41. https://doi.org/10.1142/S1793536909000047.
Wu, Z. H., and N. E. Huang. 2010. “On the filtering properties of the empirical mode decomposition.” Adv. Adapt. Data Anal. 2 (4): 397–414. https://doi.org/10.1142/S1793536910000604.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: May 8, 2019
Accepted: Jan 27, 2020
Published online: Apr 30, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 30, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.