An innovative data mining procedure, using clean algorithm and factor analysis, for irregularly sampled temporal environmental data sets

PDF (English)
Publiée : Ιαν 1, 2007
DOI : https://doi.org/10.12681/bgsg.17225
E. Fakiris
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras
G. Papatheodorou
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras
P. Panagiotopoulos
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras
Résumé

Environmental data are often irregularly collected in the time domain due to various reasons which affect the field sampling schedule. As a result, data sets with uneven time step and time periods with no measurements are frequently built. Many problems occur in such data sets when processed owing to that neither statistical nor spectral analysis methods can easily be applied to them without any specific pre-treatment. In our study it is demonstrated a unified methodological scheme especially designed to deal with incomplete and unevenly sampled temporal data sets. This method consists of the CLEAN algorithm and the Factor analysis. The proposed methodology is successfully applied to data sets that belong to two sampling sites of the Greek river Strimonas

Article Details
  • Rubrique
  • New Technologies in Geophysical and Geological Research
Creative Commons License

Ce travail est disponible sous licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International.

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution Non-Commercial License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (preferably in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Téléchargements
Les données relatives au téléchargement ne sont pas encore disponibles.
Références
Baisch, S., and Bokelman, G.H.R., 1999. Spectral analysis with incomplete time series: an example from seismology, Computers & Geosciences, 25, 739-750.
Davis, J.C., 1986. Statistics and data analysis in geology, Wiley, New York, 647pp.
Drehar, J.W., Robert, D.H., and Lehar, J., 1986. Very large array observations of rapid nonperiodic variations in OJ287, Nature, 320, 239-242.
Duvall, T.L., Jr., and Harvey, J.W. 1984. Rotational frequency splitting of solar oscillations, Nature, 310, 19-22.
Heslop, D., and Dekkers, M.J., 2002. Spectral analysis of unevenly spaced climatic time series using CLEAN: signal recovery and derivation of significance levels using a Monte Carlo simulation, Physics of the Earth and Planetary Interiors ,130, 103-116.
Mitikka, S., and Ekholm, P., 2003. Lakes in the Finnish Eurowaternet: Status and trends, The Science of the Total Environment, 310 37^15
Negi, J.G., Tiwari, R.K., and Rao, K.N.N., 1996. Clean periodicity in secular variations of dolomite abundance in deep marine sediments, Marine Geology, 133, 113-121.
Oliver, M.A., Webster, R., Edwards, K.J., and Whittington, G., 1997. Multivariate autocorrelation and spectral analyses of a pollen profile from Scotland and evidence for periodicity, Review of Palaeobotany and Palynology, 96, 121-144.
Papatheodorou, G., et al., 2006. A long-term study of temporal hydrochemical data in a shallow lake using multivariate statistical techniques, Ecological Modelling, 193, 759-776.
Raike, Α., Pietilainen, O.-P., Rekolainen, S., Kauppila, P., Pitkanen, H., Niemi, J., Raateland, Α., and Vuorenmaa, J., 2002. Trends of phosphorus, nitrogen and chlorophyll a concentrations in Finnish rivers and lakes in 1975 -2000, The Science of the Total Environment, 310 (2003) 47 -59.
Ritzi, R.W., Wright, S.L., Mann, B., and Chen, M., 1993. Analysis of Temporal Variability in Hydrogeochemical Data Used for Multivariate Analysis, Ground Water, 31(2), 221-229.
Robert, D.H., Lehar, J., and Drever. J.W. 1987. Time series analysis with clean derivation of spectra, Astron. J., 93, 968-989.
Schulz, M., and Stattegger, K., 1997. Spectrum: spectral analysis of unevenly spaced paleoclimatic time series, Computers & Geosciences, 23(9). 929-945.
Spangenberg, Α., and Bredemeier, M., 1999. Application of spectral analysis to meteorological and soil solution chemistry data, Chemosphere, 39(10), 1651-1665.
Stefanakos, Ch. N., and Athanasoulis, G.A.. 2001. A unified methodology for the analysis, completion and simulation of nonstationary time series with missing values, with application to wave data, Applied ocean research, 23, 207-220
Suk, H., and Lee, K.K., 1999. Characterization of a Ground Water Hydrochemical System through Multivariate Analysis: Clustering into Ground Water Zones. Ground Water, 37(3), 358-366.
Tiwari, R.K., and Rao, K.N.N., (1999) Solar and tidal reverberarions of déglaciation records from the tropical western Pacific: a clean spectral approach, Geofizika, 16-17, 33-41.
Vio, R., Christiannis, Lossi O., and Provenzale, Α., (1992) Time series analysis in astronomy: An application to quasar variability studies, Astron. J., 391, 518-530.
Articles les plus lus par le même auteur ou la même autrice