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222Rn Konsantrasyon ve Depremler Arasındaki İlişkileri Açıklayan Bulanık Mantık Uygulaması

Year 2018, , 211 - 217, 16.03.2018

Miraç Kamışlıoğlu

https://doi.org/10.17776/csj.360320
Cited By: 1

Abstract

Deprem davranışları, genel olarak fiziğin lineer olmayan konuları
arasındadır. Şimdiye kadar yapılan araştırmalar maalesef deprem davranışını tahmin
etmek için tam olarak matematiksel bir model önerememektedir. Böyle bir modelin
kurulamamasının başlıca nedeni, depremin doğrusal olmayan davranış gösteriyor
olması ve oluşum mekanizmasının çeşitli faktörlere bağlı olmasından
kaynaklanmaktadır. Doğrusal olmayan sistemlerin matematiksel ifadeleri ve
modellenmesi oldukça zordur ve bazen yüksek hızlarda, geniş bellekli
bilgisayarları gerektirir. Bu nedenle, uzman sistemler olarak bilinen bulanık
mantık bu tür modellemelerde yaygın bir şekilde kullanılmaktadır. Bu model,
doğrusal veya doğrusal olmayan yönleri tanımlayan uygun matematiksel ifadeler
vasıtasıyla herhangi bir fiziksel olayın uzay-zaman davranışını incelemek için
önerilmektedir. Bulanık mantık uygulamaları son yıllarda hızlı bir artış göstermektedir.
Bulanık mantık modellemesi dinamik sistemin iç yapısı hakkında çok güçlü bir
açıklama olabilir. Deprem tahmin çalışmalarında en sık kullanılan gösterge
toprak 222Rn gazıdır. Biz bu çalışmada, 222Rn ile deprem
şiddeti arasındaki ilişkiyi bulanık mantık metodu kullanarak açıklamaya
çalıştık. Uygulama bölgesi olarak, Doğu Anadolu Fay Sisteminin yakınlarındaki
Mersin bölgesinden alınan 222Rn verilerini kullandık.

Keywords

Deprem Modelleme Radon Bulanık Mantık Uzman Sistemler

References

  • [1]. Dragovic S., Antonije O., Classification of soil samples according to geographic origin using gamma-ray spectrometry and pattern recognition methods. Appl Radiat Isotopes., 65 (2007) 218-224.
  • [2]. Külahcı F., İnceöz M., Doğru M., Aksoy E., and Baykara O., Artificial neural network model for earthquake prediction with radon monitoring., Appl Radiat Isotopes, 67 (2009) 212–219.
  • [3]. Gueldal V., Tongal H. Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting., Water Resour Manag, 24 (2010) 105-128.
  • [4]. Zadeh L. A. Fuzzy sets. Information and Control, 8 (1967) 38-53.
  • [5]. Şen, Z., Fuzzy Logic Principal and Modelling, (Engineering and Liberal art) p:11, Water Foundation Publication, İstanbul, 2009.
  • [6]. Şen, Z., Scientific thinking and mathematical modeling principles p:11, Water Foundation Publication, İstanbul, 2009.
  • [7]. Kisi O., Shiri J., Nikoofar B., Forecasting daily lake levels using artificial intelligence approaches., Comput Geosci,41 (2012) 169-180.
  • [8]. Tarakçı, M., C. Harmanşah, M.M. Saç, and M. İçhedef (2014), Investigation of the relationships between seismic activities and radon level in Western Turkey, Appl. Radiat. Isotopes 83A, (2017) 12-17,
  • [9]. Zadeh L., Kacprzyk J. (eds.) Fuzzy Logic for the Management of Uncertainty. Wiley, 1992 New York.
  • [10]. McNeill, F. M., Thro E. Fuzzy Logic: A Practical Approach. AP Professional, 1994 Boston, MA.
  • [11]. Kosko, B. Fuzzy Thinking: The New Science of Fuzzy Logic. Hyperion, 1992 New York.
  • [12]. Klir, G. J. and Fogel, T. A. Fuzzy Sets, Uncertainty and Information. Prentice Hall, 1988 New York.
  • [13]. Fuzzy Logic Toolbox, For Use With MATLAB®, User Guide. 2006 Math Works, Inc.
  • [14]. Kamışlıoğlu M., and Külahcı F., Chaotic Behavior of Soil Radon Gas and Applications, Acta Geophysica, 64-5 (2016) 1563-1592.

A Fuzzy Logic Application for Explain Relationships Between 222Rn Concentration and Earthquakes

Year 2018, , 211 - 217, 16.03.2018

Miraç Kamışlıoğlu

https://doi.org/10.17776/csj.360320
Cited By: 1

Abstract

Earthquake
behaviors are, in general, among the non-linear topics of physics. Unfortunately
researches up to now could not yet propose a complete mathematical model for
earthquake behavior prediction possibilities. The main reason for not being
able to establish such a model is due to the non-linear behavior of the
earthquake and its generation is dependent on a variety of indigenous factors.
Mathematical expressions and modeling of the non-linear systems is
comparatively difficult and sometimes requires high speed and memory computers.
For this reason, the expert systems as Fuzzy Logic (FL) are now commonly used
for such modelling. Model is suggested a system to examine the
space-time behavior of any physical phenomena through a set of convenient
mathematical expressions, which describe linear or non-linear aspects. Fuzzy
logic applications have a fast increase in past few years. Fuzzy logic
modelling can be a very powerful explains about internal structure of dynamic
system. The most commonly used indicator in earthquake prediction studies is
soil radon gas (222Rn). In this study, we have tried to explain
relationships between 222Rn and earthquakes using fuzzy logic. The
application region is performed for 222Rn data of Mersin region near
the East Anatolian Fault System, Turkey.

Keywords

Earthquake Modelling Radon Fuzzy Logic

References

  • [1]. Dragovic S., Antonije O., Classification of soil samples according to geographic origin using gamma-ray spectrometry and pattern recognition methods. Appl Radiat Isotopes., 65 (2007) 218-224.
  • [2]. Külahcı F., İnceöz M., Doğru M., Aksoy E., and Baykara O., Artificial neural network model for earthquake prediction with radon monitoring., Appl Radiat Isotopes, 67 (2009) 212–219.
  • [3]. Gueldal V., Tongal H. Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting., Water Resour Manag, 24 (2010) 105-128.
  • [4]. Zadeh L. A. Fuzzy sets. Information and Control, 8 (1967) 38-53.
  • [5]. Şen, Z., Fuzzy Logic Principal and Modelling, (Engineering and Liberal art) p:11, Water Foundation Publication, İstanbul, 2009.
  • [6]. Şen, Z., Scientific thinking and mathematical modeling principles p:11, Water Foundation Publication, İstanbul, 2009.
  • [7]. Kisi O., Shiri J., Nikoofar B., Forecasting daily lake levels using artificial intelligence approaches., Comput Geosci,41 (2012) 169-180.
  • [8]. Tarakçı, M., C. Harmanşah, M.M. Saç, and M. İçhedef (2014), Investigation of the relationships between seismic activities and radon level in Western Turkey, Appl. Radiat. Isotopes 83A, (2017) 12-17,
  • [9]. Zadeh L., Kacprzyk J. (eds.) Fuzzy Logic for the Management of Uncertainty. Wiley, 1992 New York.
  • [10]. McNeill, F. M., Thro E. Fuzzy Logic: A Practical Approach. AP Professional, 1994 Boston, MA.
  • [11]. Kosko, B. Fuzzy Thinking: The New Science of Fuzzy Logic. Hyperion, 1992 New York.
  • [12]. Klir, G. J. and Fogel, T. A. Fuzzy Sets, Uncertainty and Information. Prentice Hall, 1988 New York.
  • [13]. Fuzzy Logic Toolbox, For Use With MATLAB®, User Guide. 2006 Math Works, Inc.
  • [14]. Kamışlıoğlu M., and Külahcı F., Chaotic Behavior of Soil Radon Gas and Applications, Acta Geophysica, 64-5 (2016) 1563-1592.
There are 14 citations in total.

Details

Primary Language English
Journal Section Natural Sciences
Authors

Miraç Kamışlıoğlu

Publication Date March 16, 2018
Submission Date December 2, 2017
Acceptance Date January 31, 2018
Published in Issue Year 2018

Cite

APA Kamışlıoğlu, M. (2018). A Fuzzy Logic Application for Explain Relationships Between 222Rn Concentration and Earthquakes. Cumhuriyet Science Journal, 39(1), 211-217. https://doi.org/10.17776/csj.360320

Cited By

Fuzzy reasoning in the investigation of seismic behavior
Mathematical Methods in the Applied Sciences
https://doi.org/10.1002/mma.6184