Acknowledgement
본 연구는 독도연구사업(PG-52262)의 지원을 받아 수행되었으며, 연구비 지원에 감사드립니다. 또한 해상 모니터링 자료를 제공해주신 기상청에 감사드립니다.
References
- Afrifa-Yamoah, E., Mueller, U.A., Taylor, S.M. and Fisher, A.J. (2020). Missing data imputation of high-resolution temporal climate time series data. Meteorological Applications, https://doi.org/10.1002/met.1873.
- Almendra-martin, L., Martinez-Fernandez, J., Piles, M. and Gonzalez-Zamora, A. (2021). Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe. Remote Sensing of Environment, 258, https://doi.org/10.1016/j.rse.2021.112377.
- Baddoo, T.D., Li, Z., Odai, S.N., Boni. K.R.C., Nooni, I.K. and Andam-Akorful, S.A. (2021). Comparison of missing data infilling mechanisms for recovering a real-world single station streamflow observation. International J. of Environmental research and Public Health, 18, https://doi.org/10.3390/ijerph18168375.
- Bellido-Jimenez, J.A., Gualda, J.E. and Garcia-Marin, A.P. (2021). Assessing machine learning models for gap-filling daily rainfall series in a semiarid region of Spain, Atmosphere, 12, 1158. https://doi.org/10.3390/atmos12091158.
- Cho, H.Y., Oh, J.H., Kom, K.O. and Shin, J.S. (2013). Outlier detection and missing data filling methods for coastal water temperature data. J. of Coastal Research, Sp[ecial Issue No. 65, 1898-1903.
- Grolemund, G. and Wickham, H. (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25. https://www.jstatsoft.org/v40/i03/.
- Hair, Jr. J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis, A Global Perspective, Seventh Edition, Chapter 2, Pearson.
- Kandasamy, S., Baret, F., Verger, A., Neveux, P. and Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products. Biogeosciences, 10, 4055-4071, https://doi.org/10.5194/bg-10-4055-2013.
- Kang, M., Ichii, K., Kim, J., Indrawati, Y.M., Park, J., Moon, M., Lim, J.-H. and Chun, J.-H. (2019). New gap-filling strategies for long-period flux data gaps using a data-driven approach. Atmosphere, 10, 568, https://doi.org/10.3390/atmos10100568.
- Liu, X. and Wang, M. (2019). Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using DINEOF method. Remote Sensing, 11, https://doi.org/10.3390/rs11020178.
- Millard, S.P. (2013). EnvStats: An R Package for Environmental Statistics. Springer, New York.
- Moritz, S and Bartz-Beielstein, T. (2017). imputeTS: Time Series Missing Value Imputation in R. The R Journal, 9(1), 207-218. https://doi.org/10.32614/RJ-2017-009.
- Golyandina, N. and Korobeynikov, A. (2014) Basic Singular Spectrum Analysis and Forecasting with R. Computational Statistics and Data Analysis, 71, 934-954. https://doi.org/10.1016/j.csda.2013.04.009
- Fredj, E., Roarty, H., Kohut, J., Smith, M. and Glenn, S. (2016). Gap filling of the coastal ocean surface currents from HFR data: Application to the Mid-Atlantic Bight HFR Network. Journal of Atmospheric and Oceanic Technology, 33(6), 1097-1111. https://doi.org/10.1175/JTECH-D-15-0056.1
- Rumaling, M.I., Chee, F.P., Dayou, J., Chang, J.H.W., Kong, S.S.K. and Sentian, J. (2020). Missing value imputation for PM10 concentration in Sabah using nearest neighbour method (NNM) and expectation-maximization (EM) algorithm. Asian Journal of Atmospheric Environment, 14(1), 62-72. https://doi.org/10.5572/ajae.2020.14.1.062
- Sarafanov, M., Kazakov, E., Nikitin, N.O. and Kalyuzhnaya, A.V. (2020). A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and NDVI. Remote Sensing, 12, https://doi.org/10.3390/rs12233865.
- Sattari, M.T., Falsafian, K., Irvem, A., Shahav, S. and Qasem, S.N. (2020) Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall. Engineering Applications of Computational Fluid Mechanics, 14(1), 1078-1094. https://doi.org/10.1080/19942060.2020.1803971.
- Sim, J., Lee, J.S. and Kwon, B. (2015). Missing values and optimal selection of an imputation method and classfication algorothm to improve the accuracy of ubiquitous computing applications. Mathematical Problems in Engineering, 2015, http://dx.doi.org/10.1155/2015/538613.
- Velasco-Gallego, C. and Lazakis, I. (2020). Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study, Ocean Engineering, 218. https://doi.org/10.1016/j.oceaneng.2020.108261.
- Wand, M.P. (2021). KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995). R package version 2.23-20. https://CRAN.R-project.org/package=KernSmooth.
- Wand, M.P. and Jones, M.C. (1995). Kernel Smoothing. Chapman and Hall, London.
- Wang, G., Ma, M., Jinag, L., Chen, F. and Xu, L. (2021). Multiple imputation of marine search and rescue data at multiple missingpatterns. PLOS ONE, 16(6), https://doi.org/10.1371/journal.pone.0252129.
- Zhao, X. and Huang, Y. (2015). A comparison of the three gap filling techniques for eddy covariance net carbon fluxes in short vegetation ecosystems, 2015. http://dx.doi.org/10.1155/2015/260580.