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Zaman Serisi Kümelemesinin Türkiye’deki hava kirliliği izleme istasyonlarındaki bilgi fazlalığının tespit edilmesine uygulanması

Year 2016, Volume: 20 Issue: 3, 605 - 616, 02.09.2016

Öznur İşçi Güneri Nevin Güler Dinçer Muhammet Oğuzhan Yalçın

https://doi.org/10.16984/saufenbilder.69439

Abstract

Bu çalışmanın amacı Türkiye’de PM10 ve SO2 kirleticileri konsantrasyonları bakımından benzer davranışa sahip hava kirliliği izleme istasyonlarını belirlemek ve böylece izleme maliyetini ve bilgi fazlalığını azaltmaktır. Bu amaca yönelik olarak, otoregresif modele dayanan Bulanık k-Medoidler (BKM) algoritması kullanılmıştır. Yapılan analizler sonucunda, izleme istasyonlarındaki bilgi fazlalılığının ve bununla birlikle izleme maliyetinin PM10 hava kirleticisi için yaklaşık olarak %78.5, SO2 hava kirleticisi için  %73.5 azaltılabileceği sonucunda ulaşılmıştır.

Keywords

Otoregresif model bulanık k-medoid kümeleme hava kalitesi izleme istasyonları

References

  • Ö. Akyürek, O. Arslan ve A. Karedemir, «SO2 ve PM10 Hava Kirliliği Parametrelerinin CBS ile Konumsal Analizi: Kocaeli Örneği,» TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara, 2013.
  • D. A. Dickey ve W. A. Fuller, «Distribution of the Estimators for Autoregressive Time Series with a Unit Root,» Journal of the American Statistical Association, no. 74, pp. 427-431, 1979.
  • C. Lavecchia, E. Angelino, M. Bedogni, E. Brevetti, R. Gualdi, G. Lanzani, A. Musitelli ve M. Valentini, «The Ozone Patterns in the Aerological Basin Of Milan (Italy),» Environ Softw, no. 11, pp. 73-80, 1996.
  • C. Ortuno, M. Jaimes, R. Munoz, R. Ramos ve V. H. Paramo, «Redundancy Analysis for the Mexico City Air Monitoring Network: The Case of CO.,» 1997. [Çevrimiçi]. Available: http://files.abstractsonline.com/CTRL/2D/A/06E/7F9/022/434/F8D/F8C/2D3/E4B/F3E/66/a1177_1.doc. [tarihinde erişilmiştir 06 13 2016].
  • C. Silva ve A. Quiroz, «Optimization of The Atmospheric Pollution Monitoring Network at Santiago De Chile,» Atmos Environ,, no. 37, pp. 2337-2345, 2003.
  • S. Saksena, V. Joshi ve R. S. Patil, «Cluster Analysis of Delhi’s Ambient Air Quality Data,» J Environ Monit, no. 5, pp. 491-499, 2003.
  • V. V. M. Gabushi, V. Gabushi ve M. Volta, «A Methodology for Seasonal Photochemical Model Simulation Assessment,» J Environ Pollut, no. 24, pp. 11-21, 2005.
  • W. Z. Lu, H. D. He ve L. Y. Dong, «Performance Assessment of Air Quality Monitoring Networks Using Principal Component Analysis and Cluster Analysis,» Build Environ, no. 46, pp. 577-583, 2011.
  • E. Gramsh, F. Cereceda-Balic, P. Oyola ve D. Von Baer, «Examination of Pollution Trends in Santiago De Chile With Cluster Analysis of PM10 and Ozone Data,» Atmos Environ, no. 40, pp. 5464-5475, 2006.
  • I. Morlini, «Searching for Structure in Measurements of Air Pollutant Concentration,» Environmetrics, no. 18, pp. 823-840, 2007.
  • D. Giri, V. Murthy, P. R. Adhikary ve N. Khanal, «Ambient Air Quality of Kathmandu Valley as Reflected by Atmospheric Particulate Matter Concentrations PM10,» International Journal of Environmental Science and Technology, no. 9, 2006.
  • J. M. Pires, S. V. Sousa, M. C. Pereira, M. M. Alvim-Ferraz ve F. G. Martins, «Management of Air Quality Monitoring Using Principal Component and Cluster Analysis—Part I: SO2 and PM10,» Atmos Environ, no. 42, pp. 1249-1260, 2008a.
  • J. M. Pires, S. V. Sousa, M. C. Pereira, M. M. Alvim-Ferraz ve F. G. Martins, «Management of Air Quality Monitoring Using Principal Component and Cluster Analysis—Part II: CO, NO2 and O3,» Atmos Environ, no. 42, pp. 1261-1274, 2008b.
  • R. Ignaccolo, S. Ghigo ve E. Giovenali, «Analysis of Monitoring Networks by Functional Clustering,» Environmetrics, no. 62, pp. 672-686, 2008.
  • J. Lau, W. T. Hung ve C. S. Cheung, «Interpretation of Air Quality in Relation to Monitoring Station’s Surroundings,» Atmos Environ, no. 43, pp. 769-777, 2009.
  • P. D'Urso ve E. A. Maharaj, «Autocorrelation-Based Fuzzy Clustering of Time Series,» Fuzzy Sets Syst, no. 160, pp. 3565-3589, 2009.
  • P. D'Urso ve E. A. Maharaj, «Wavelets-Based Clustering of Multivariate Time Series,» Fuzzy Sets and Systems, no. 193, pp. 33-361, 2012.
  • P. D'Urso, D. Di Lallo ve E. A. Maharaj, «Autoregressive Model-Based Fuzzy Clustering and its Application for Detecting Information Redundancy in Air Pollution Monitoring Network,» Soft Computing, cilt 1, no. 17, pp. 13-83, 2013.
  • A. Iizuka, S. Shirato, A. Mizukoshi, M. Noguchi, A. Yamasaki ve Y. Yanagisawa, «A Cluster Analysis of Constant Ambient Air Monitoring Data from the Kanto Region of Japan,» Int. J. Environ. Res. Public Health, cilt 7, no. 11, pp. 6844-6855, 2014.
  • G. B. Box, G. M. Jenkins, G. C. Reinsel ve L. M. Liu, Time Series Analysis, Pearson Education, 2009.
  • L. Xie ve G. Beni, «A Validity Measure for Fuzzy Clustering,» IEEE Transactions on Pattern Analysis and Machine Intelligence, cilt 8, no. 13, pp. 841-847, 1991.
  • L. Kaufman ve P. J. Rousseuw, Clustering by Means of Medoids. in: Y. Dodge (Ed.), Statistical Data Analysis based on the L1 Norm (North-Holland, Amsterdam, 1987), 1987, pp. 405-416.

Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey

Year 2016, Volume: 20 Issue: 3, 605 - 616, 02.09.2016

Öznur İşçi Güneri Nevin Güler Dinçer Muhammet Oğuzhan Yalçın

https://doi.org/10.16984/saufenbilder.69439

Abstract

The aim of study is to determine the monitoring stations having similar behavior with respect to PM10 and SO2 concentrations and thus decrease monitoring cost and information redundancy. For this purpose, autoregressive model based Fuzzy K-medoids algorithm is used. At the results of analyses, it has been concluded that information redundancy in monitoring stations and thus monitoring cost can be decreased approximately 78.5% for PM10 air pollutant, 73.5% for SO2 air pollutant.  

Keywords

Autoregressive model fuzzy k-medoids clustering air quality monitoring stations

References

  • Ö. Akyürek, O. Arslan ve A. Karedemir, «SO2 ve PM10 Hava Kirliliği Parametrelerinin CBS ile Konumsal Analizi: Kocaeli Örneği,» TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara, 2013.
  • D. A. Dickey ve W. A. Fuller, «Distribution of the Estimators for Autoregressive Time Series with a Unit Root,» Journal of the American Statistical Association, no. 74, pp. 427-431, 1979.
  • C. Lavecchia, E. Angelino, M. Bedogni, E. Brevetti, R. Gualdi, G. Lanzani, A. Musitelli ve M. Valentini, «The Ozone Patterns in the Aerological Basin Of Milan (Italy),» Environ Softw, no. 11, pp. 73-80, 1996.
  • C. Ortuno, M. Jaimes, R. Munoz, R. Ramos ve V. H. Paramo, «Redundancy Analysis for the Mexico City Air Monitoring Network: The Case of CO.,» 1997. [Çevrimiçi]. Available: http://files.abstractsonline.com/CTRL/2D/A/06E/7F9/022/434/F8D/F8C/2D3/E4B/F3E/66/a1177_1.doc. [tarihinde erişilmiştir 06 13 2016].
  • C. Silva ve A. Quiroz, «Optimization of The Atmospheric Pollution Monitoring Network at Santiago De Chile,» Atmos Environ,, no. 37, pp. 2337-2345, 2003.
  • S. Saksena, V. Joshi ve R. S. Patil, «Cluster Analysis of Delhi’s Ambient Air Quality Data,» J Environ Monit, no. 5, pp. 491-499, 2003.
  • V. V. M. Gabushi, V. Gabushi ve M. Volta, «A Methodology for Seasonal Photochemical Model Simulation Assessment,» J Environ Pollut, no. 24, pp. 11-21, 2005.
  • W. Z. Lu, H. D. He ve L. Y. Dong, «Performance Assessment of Air Quality Monitoring Networks Using Principal Component Analysis and Cluster Analysis,» Build Environ, no. 46, pp. 577-583, 2011.
  • E. Gramsh, F. Cereceda-Balic, P. Oyola ve D. Von Baer, «Examination of Pollution Trends in Santiago De Chile With Cluster Analysis of PM10 and Ozone Data,» Atmos Environ, no. 40, pp. 5464-5475, 2006.
  • I. Morlini, «Searching for Structure in Measurements of Air Pollutant Concentration,» Environmetrics, no. 18, pp. 823-840, 2007.
  • D. Giri, V. Murthy, P. R. Adhikary ve N. Khanal, «Ambient Air Quality of Kathmandu Valley as Reflected by Atmospheric Particulate Matter Concentrations PM10,» International Journal of Environmental Science and Technology, no. 9, 2006.
  • J. M. Pires, S. V. Sousa, M. C. Pereira, M. M. Alvim-Ferraz ve F. G. Martins, «Management of Air Quality Monitoring Using Principal Component and Cluster Analysis—Part I: SO2 and PM10,» Atmos Environ, no. 42, pp. 1249-1260, 2008a.
  • J. M. Pires, S. V. Sousa, M. C. Pereira, M. M. Alvim-Ferraz ve F. G. Martins, «Management of Air Quality Monitoring Using Principal Component and Cluster Analysis—Part II: CO, NO2 and O3,» Atmos Environ, no. 42, pp. 1261-1274, 2008b.
  • R. Ignaccolo, S. Ghigo ve E. Giovenali, «Analysis of Monitoring Networks by Functional Clustering,» Environmetrics, no. 62, pp. 672-686, 2008.
  • J. Lau, W. T. Hung ve C. S. Cheung, «Interpretation of Air Quality in Relation to Monitoring Station’s Surroundings,» Atmos Environ, no. 43, pp. 769-777, 2009.
  • P. D'Urso ve E. A. Maharaj, «Autocorrelation-Based Fuzzy Clustering of Time Series,» Fuzzy Sets Syst, no. 160, pp. 3565-3589, 2009.
  • P. D'Urso ve E. A. Maharaj, «Wavelets-Based Clustering of Multivariate Time Series,» Fuzzy Sets and Systems, no. 193, pp. 33-361, 2012.
  • P. D'Urso, D. Di Lallo ve E. A. Maharaj, «Autoregressive Model-Based Fuzzy Clustering and its Application for Detecting Information Redundancy in Air Pollution Monitoring Network,» Soft Computing, cilt 1, no. 17, pp. 13-83, 2013.
  • A. Iizuka, S. Shirato, A. Mizukoshi, M. Noguchi, A. Yamasaki ve Y. Yanagisawa, «A Cluster Analysis of Constant Ambient Air Monitoring Data from the Kanto Region of Japan,» Int. J. Environ. Res. Public Health, cilt 7, no. 11, pp. 6844-6855, 2014.
  • G. B. Box, G. M. Jenkins, G. C. Reinsel ve L. M. Liu, Time Series Analysis, Pearson Education, 2009.
  • L. Xie ve G. Beni, «A Validity Measure for Fuzzy Clustering,» IEEE Transactions on Pattern Analysis and Machine Intelligence, cilt 8, no. 13, pp. 841-847, 1991.
  • L. Kaufman ve P. J. Rousseuw, Clustering by Means of Medoids. in: Y. Dodge (Ed.), Statistical Data Analysis based on the L1 Norm (North-Holland, Amsterdam, 1987), 1987, pp. 405-416.
There are 22 citations in total.

Details

Journal Section Research Articles
Authors

Öznur İşçi Güneri

Nevin Güler Dinçer

Muhammet Oğuzhan Yalçın

Publication Date September 2, 2016
Submission Date June 16, 2016
Published in Issue Year 2016 Volume: 20 Issue: 3

Cite

APA İşçi Güneri, Ö., Güler Dinçer, N., & Yalçın, M. O. (2016). Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. Sakarya University Journal of Science, 20(3), 605-616. https://doi.org/10.16984/saufenbilder.69439
AMA İşçi Güneri Ö, Güler Dinçer N, Yalçın MO. Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. SAUJS. November 2016;20(3):605-616. doi:10.16984/saufenbilder.69439
Chicago İşçi Güneri, Öznur, Nevin Güler Dinçer, and Muhammet Oğuzhan Yalçın. “Time Series Clustering’s Application to Identifying of Information Redundancy at Air Pollution Monitoring Stations in Turkey”. Sakarya University Journal of Science 20, no. 3 (November 2016): 605-16. https://doi.org/10.16984/saufenbilder.69439.
EndNote İşçi Güneri Ö, Güler Dinçer N, Yalçın MO (November 1, 2016) Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. Sakarya University Journal of Science 20 3 605–616.
IEEE Ö. İşçi Güneri, N. Güler Dinçer, and M. O. Yalçın, “Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey”, SAUJS, vol. 20, no. 3, pp. 605–616, 2016, doi: 10.16984/saufenbilder.69439.
ISNAD İşçi Güneri, Öznur et al. “Time Series Clustering’s Application to Identifying of Information Redundancy at Air Pollution Monitoring Stations in Turkey”. Sakarya University Journal of Science 20/3 (November 2016), 605-616. https://doi.org/10.16984/saufenbilder.69439.
JAMA İşçi Güneri Ö, Güler Dinçer N, Yalçın MO. Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. SAUJS. 2016;20:605–616.
MLA İşçi Güneri, Öznur et al. “Time Series Clustering’s Application to Identifying of Information Redundancy at Air Pollution Monitoring Stations in Turkey”. Sakarya University Journal of Science, vol. 20, no. 3, 2016, pp. 605-16, doi:10.16984/saufenbilder.69439.
Vancouver İşçi Güneri Ö, Güler Dinçer N, Yalçın MO. Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey. SAUJS. 2016;20(3):605-16.

Sakarya University Journal of Science (SAUJS)