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1999 Marmara Depremi ve Güneş Tutulmasının Naive Bayes Sınıflayıcısı ile İstatistiksel Analizi

Year 2021, Issue: 23, 643 - 648, 30.04.2021
https://doi.org/10.31590/ejosat.876223

Abstract

İyonosfer, atmosferin 50 ila 1000 km yükseklikleri arasında yer alan, güneşten gelen radyasyonla plazma durumuna iyonize olmuş önemli bir katmanıdır. İyonosferik plazmanın en belirleyici parametresi, güneş, jeomanyetik ve sismik hareketlilikle ve güneş patlamaları, güneş lekesi sayısı, güneş rüzgârı, jeomanyetik fırtınalarla değişkenlik ve bağlaşım gösteren elektron yoğunluğudur. Elektron yoğunluğunun ölçülebilir önemli bir niceliği de, iyonosfer ve üst atmosferin yapısını araştırmak için etkili bir yol sağlayan Toplam Elektron İçeriği’dir (TEİ). TEİ, bir ışın yolu boyunca elektron yoğunluğunun çizgi integrali veya bir ışın yolu boyunca toplam elektron sayısı olarak tanımlanmaktadır. İyonosferin uzamsal-zamansal değişkenliği, ayrıca, uzamsal-zamansal yönsemeler ve jeomanyetik alandaki bozulmalar, yerçekimi dalgaları ve sismik aktivitelerin üst atmosfere ve iyonosfere bağlaşımından da etkilenmektedir. Bu değişkenliklerin bazıları iyonosferde belirli bir frekans, süre ve hızda yayılan dalga benzeri salınımlar üretir. Bu çalışmada, sismik, güneş ve jeomanyetik hareketliliğe bağlı olarak iyonosferde meydana gelen bozulmaların ve iyonosferin sakin olarak nitelendirilen durumundan sapmaların tespiti için Naive Bayes Sınıflandırıcısı kullanılmıştır. Naive Bayes Sınıflandırıcısı, Türkiye üzerinde konumlandırılmış Yerküresel Konumlama Sistemi (YKS) istasyonlarından 1999 yılında meydana gelen güneş tutulması ve Marmara Depremi periyodunca kestirilen TEİ verilerine uygulanmıştır.

Thanks

Bu çalışmada, IONOLAB-TEC'in hesaplanmasında kullanılan GIM-TEC, Satellite DCB ve efemeris verileri, ftp://cddis.gsfc.nasa.gov/pub/gps/products/ionex adresindeki IGS Analysis Center of Jet Propulsion Laboratory (JPL)’den sağlanmıştır. Yazarlar, makaleyi geliştirmede yazarlar için çok yararlı ve yapıcı olan yorumları ve katkıları için anonim hakemlere teşekkür eder. Yazarlar son olarak, Prof. Dr. Feza Arıkan ve IONOLAB grubuna IONOLAB-BIAS ve IONOLAB-TEC Algoritması üzerindeki üstün emeklerinden dolayı teşekkür eder.

References

  • Arikan, F., Erol, C., & Arikan, O. (2003). Regularized estimation of vertical total electron content from Global Positioning System data. Space Physics, 108(A12), 1-12. doi:10.1029/2002JA009605
  • Arikan, F., Erol, C., & Arikan, O. (2004). Regularized estimation of vertical total electron content from GPS data for a desired time period. Radio Science, 39(6), 1-10. doi:10.1029/2004RS003061
  • Arikan, F., Nayir, H., Sezen, U., & Arikan, O. (2008). Estimation of single station interfrequency receiver bias using GPS‐TEC. Radio Science, 43(4), 1-13. doi:10.1029/2007RS003785
  • Budak, C., Turk, M., & Toprak, A. (2016). Removal of impulse noise in digital images with naive Bayes classifier method. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2717 – 2729. doi:10.3906/elk-1401-57
  • Chen, Y., Liu, J., Tsai, Y., & Chen, C. (2004). Statistical Tests for Pre-earthquake Ionospheric Anomaly. Terrestrial Atmospheric and Oceanic Sciences, 15(3), 385-396. doi:10.3319/TAO.2004.15.3.385(EP)
  • Domingos, P., & Pazzani, M. (1997). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Machine Learning,, 29, 103–130.
  • Efendi, E., & Arikan, F. (2017). A fast algorithm for automatic detection of ionospheric disturbances: DROT. Advances in Space Research, 59(12), 2923-2933. doi:10.1016/j.asr.2017.03.018
  • Fuller-Rowell, T., Codrescu, M., & Wilkinson, P. (2000). Quantitative modeling of the ionospheric response to geomagnetic activity. Annales Geophysicae, 18, 766–781. doi:10.1007/s00585-000-0766-7
  • Hand, D., & Yu, K. (2007). Idiot's Bayes: Not So Stupid after All? International Statistical Review, 69(3), 385 - 398. doi:10.1111/j.1751-5823.2001.tb00465.x
  • IONOLAB. IONOLAB: www.ionolab.org adresinden alındı
  • Karatay, S. (2020). Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algori. Journal of Geodesy, 94(89), 1-24. doi:10.1007/s00190-020-01416-1
  • Karatay, S., Arikan, F., & Arikan, O. (2010). Investigation of total electron content variability due to seismic and geomagnetic disturbances in the ionosphere. Radio Science, 45(5), 1-12. doi:10.1029/2009RS004313
  • Karatay, S., Cinar, A., & Arikan, F. (2017). Ionospheric responses during equinox and solstice periods over Turkey. Advances in Space Research, 60(9), 1958-1967. doi:10.1016/j.asr.2017.07.038
  • Korte, M., Lühr, H., Förster, M., Haak, V., & Bencze, P. (2001). Did the solar eclipse of August 11, 1999, show a geomagnetic effect? Journal of Geophysical Research, 106(A9), 18563-18575. doi:10.1029/2001JA900006
  • Krankowski, A. l., Shagimuratov, I., Baran, L., Ephishov, I., & Tepenitzyna, N. (2006). The occurrence of polar cap patches in TEC fluctuations detected using GPS measurements in southern hemisphere. Advances in Space Research, 38(11), 2601-2609. doi:10.1016/j.asr.2005.12.006
  • Le, H., Liu, L., Yue, X., & Wan, W. (2008). The ionospheric responses to the 11 August 1999 solar eclipse: observations and modeling. Annales Geophysicae, 26(1), 107-116. doi:10.5194/angeo-26-107-2008
  • Liu, J., Chen, Y., Pulinets, S., Tsai, Y., & Chuo, Y. (2000). Seismo‐ionospheric signatures prior to M≥6.0 Taiwan earthquakes. Geophysical Research Letters, 27(16), 3113-3116. doi:10.1029/2000GL011395
  • NOAA. NOAA: ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/ adresinden alındı
  • Ozcan, O., & Aydogdu, M. (2004). Possible effects of the total solar eclipse of August 11, 1999 on the geomagnetic field variations over Elaziǧ-Turkey. Journal of Atmospheric and Solar-Terrestrial Physics, 66(11), 997-1000. doi:10.1016/j.jastp.2004.03.009
  • Papoulis, A. (1984). Probability, Random Variables, and Stochastic Processes. New York, USA: McGraw-Hill
  • Pulinets, S., Legen'ka, A., Gaivoronskaya, T., & Depue, V. (2003). Main phenomenological features of ionospheric precursors of strong earthquakes. Journal of Atmospheric and Solar-Terrestrial Physics, 65(16-18), 1337-1347. doi:10.1016/j.jastp.2003.07.011
  • Scikit-Learn. Scikit-Learn. Scikit-Learn: https://scikit-learn.org/stable/modules/naive_bayes.html#out-of-core-naive-bayes-model-fitting adresinden alındı
  • Sezen, U., Arikan, F., Arikan, O., Ugurlu, O., & Sadeghimorad, A. (2013). Online, automatic, near‐real time estimation of GPS‐TEC: IONOLAB‐TEC. Space Weather, 11(5), 297–305. doi:10.1002/swe.20054
  • USGS. USGS: https://earthquake.usgs.gov/earthquakes adresinden alındı
  • Webb, G., Boughton, J., & Wang, Z. (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning, 58-1, 5-24. doi:doi.org/10.1007/s10994-005-4258-6
  • World Data Center for Geomagnetism. World Data Center for Geomagnetism: http://wdc.kugi.kyoto-u.ac.jp/index.html adresinden alındı

Statistical Analysis of 1999 Marmara Earthquake and Solar Eclipse with Naive Bayes Classifier

Year 2021, Issue: 23, 643 - 648, 30.04.2021
https://doi.org/10.31590/ejosat.876223

Abstract

The ionosphere is an important layer of the atmosphere between 50 and 1000 km altitudes, which isionized to the plasma state by radiation from the sun. The most determining parameter of ionospheric plasma is the electron density, which varies and correlates with solar, geomagnetic and seismic activity and solar flares, Sun Spots Number, solar wind and geomagnetic storms. An important measurable quantity of electron density is the Total Electron Content (TEC), which provides an efficient way to investigate the structure of the ionosphere and upper atmosphere. TEC is defined as the line integral of electron density along a beam path or the total number of electrons along a beam path. The spatio-temporal variability of the ionosphere is also affected by the spatial and temporal trends and disturbances in the geomagnetic field, gravitational waves and seismic activity coupled to the upper atmosphere and ionosphere. Some of these variations produce wave-like oscillations propagating in the ionosphere at a certain frequency, duration and speed. In this study, the Naive Bayes Classifier is used to detect disturbances in the ionosphere due to seismic, solar and geomagnetic activities and deviations from the quite state of the ionosphere. Naive Bayes Classifier is applied to TEC values obtained from Global Positioning System (GPS) stations during 1999 solar eclipse and the Marmara earthquake periods.

References

  • Arikan, F., Erol, C., & Arikan, O. (2003). Regularized estimation of vertical total electron content from Global Positioning System data. Space Physics, 108(A12), 1-12. doi:10.1029/2002JA009605
  • Arikan, F., Erol, C., & Arikan, O. (2004). Regularized estimation of vertical total electron content from GPS data for a desired time period. Radio Science, 39(6), 1-10. doi:10.1029/2004RS003061
  • Arikan, F., Nayir, H., Sezen, U., & Arikan, O. (2008). Estimation of single station interfrequency receiver bias using GPS‐TEC. Radio Science, 43(4), 1-13. doi:10.1029/2007RS003785
  • Budak, C., Turk, M., & Toprak, A. (2016). Removal of impulse noise in digital images with naive Bayes classifier method. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2717 – 2729. doi:10.3906/elk-1401-57
  • Chen, Y., Liu, J., Tsai, Y., & Chen, C. (2004). Statistical Tests for Pre-earthquake Ionospheric Anomaly. Terrestrial Atmospheric and Oceanic Sciences, 15(3), 385-396. doi:10.3319/TAO.2004.15.3.385(EP)
  • Domingos, P., & Pazzani, M. (1997). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Machine Learning,, 29, 103–130.
  • Efendi, E., & Arikan, F. (2017). A fast algorithm for automatic detection of ionospheric disturbances: DROT. Advances in Space Research, 59(12), 2923-2933. doi:10.1016/j.asr.2017.03.018
  • Fuller-Rowell, T., Codrescu, M., & Wilkinson, P. (2000). Quantitative modeling of the ionospheric response to geomagnetic activity. Annales Geophysicae, 18, 766–781. doi:10.1007/s00585-000-0766-7
  • Hand, D., & Yu, K. (2007). Idiot's Bayes: Not So Stupid after All? International Statistical Review, 69(3), 385 - 398. doi:10.1111/j.1751-5823.2001.tb00465.x
  • IONOLAB. IONOLAB: www.ionolab.org adresinden alındı
  • Karatay, S. (2020). Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algori. Journal of Geodesy, 94(89), 1-24. doi:10.1007/s00190-020-01416-1
  • Karatay, S., Arikan, F., & Arikan, O. (2010). Investigation of total electron content variability due to seismic and geomagnetic disturbances in the ionosphere. Radio Science, 45(5), 1-12. doi:10.1029/2009RS004313
  • Karatay, S., Cinar, A., & Arikan, F. (2017). Ionospheric responses during equinox and solstice periods over Turkey. Advances in Space Research, 60(9), 1958-1967. doi:10.1016/j.asr.2017.07.038
  • Korte, M., Lühr, H., Förster, M., Haak, V., & Bencze, P. (2001). Did the solar eclipse of August 11, 1999, show a geomagnetic effect? Journal of Geophysical Research, 106(A9), 18563-18575. doi:10.1029/2001JA900006
  • Krankowski, A. l., Shagimuratov, I., Baran, L., Ephishov, I., & Tepenitzyna, N. (2006). The occurrence of polar cap patches in TEC fluctuations detected using GPS measurements in southern hemisphere. Advances in Space Research, 38(11), 2601-2609. doi:10.1016/j.asr.2005.12.006
  • Le, H., Liu, L., Yue, X., & Wan, W. (2008). The ionospheric responses to the 11 August 1999 solar eclipse: observations and modeling. Annales Geophysicae, 26(1), 107-116. doi:10.5194/angeo-26-107-2008
  • Liu, J., Chen, Y., Pulinets, S., Tsai, Y., & Chuo, Y. (2000). Seismo‐ionospheric signatures prior to M≥6.0 Taiwan earthquakes. Geophysical Research Letters, 27(16), 3113-3116. doi:10.1029/2000GL011395
  • NOAA. NOAA: ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/ adresinden alındı
  • Ozcan, O., & Aydogdu, M. (2004). Possible effects of the total solar eclipse of August 11, 1999 on the geomagnetic field variations over Elaziǧ-Turkey. Journal of Atmospheric and Solar-Terrestrial Physics, 66(11), 997-1000. doi:10.1016/j.jastp.2004.03.009
  • Papoulis, A. (1984). Probability, Random Variables, and Stochastic Processes. New York, USA: McGraw-Hill
  • Pulinets, S., Legen'ka, A., Gaivoronskaya, T., & Depue, V. (2003). Main phenomenological features of ionospheric precursors of strong earthquakes. Journal of Atmospheric and Solar-Terrestrial Physics, 65(16-18), 1337-1347. doi:10.1016/j.jastp.2003.07.011
  • Scikit-Learn. Scikit-Learn. Scikit-Learn: https://scikit-learn.org/stable/modules/naive_bayes.html#out-of-core-naive-bayes-model-fitting adresinden alındı
  • Sezen, U., Arikan, F., Arikan, O., Ugurlu, O., & Sadeghimorad, A. (2013). Online, automatic, near‐real time estimation of GPS‐TEC: IONOLAB‐TEC. Space Weather, 11(5), 297–305. doi:10.1002/swe.20054
  • USGS. USGS: https://earthquake.usgs.gov/earthquakes adresinden alındı
  • Webb, G., Boughton, J., & Wang, Z. (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning, 58-1, 5-24. doi:doi.org/10.1007/s10994-005-4258-6
  • World Data Center for Geomagnetism. World Data Center for Geomagnetism: http://wdc.kugi.kyoto-u.ac.jp/index.html adresinden alındı
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Seçil Karatay 0000-0002-1942-6728

Muna Algahanı This is me

Publication Date April 30, 2021
Published in Issue Year 2021 Issue: 23

Cite

APA Karatay, S., & Algahanı, M. (2021). 1999 Marmara Depremi ve Güneş Tutulmasının Naive Bayes Sınıflayıcısı ile İstatistiksel Analizi. Avrupa Bilim Ve Teknoloji Dergisi(23), 643-648. https://doi.org/10.31590/ejosat.876223