Yapay Sinir Ağları (YSA) Yöntemi Kullanarak Ra-226, Th-232 ve U-238 Konsantrasyonlarının Kestirimleri

Yıl 2018, Cilt: 39 Sayı: 1, 87 - 94, 16.03.2018

Sevim Bilici Miraç Kamışlıoğlu Ahmet Bilici Fatih Külahcı

https://doi.org/10.17776/csj.359924

Cited By: 2

Öz

Bir bölgedeki radyoaktif çekirdek
konsantrasyonlarının belirlenmesi ve modellenmesi radyolojik tehlikeler
açısından bölge için oldukça önemlidir. ANN büyük verilere sahip sistemleri
başarılı şekilde modelleyebilir. Önerilen ANN modelinin sisteme daha önce hiç girilmemiş
verileri girilerek modelin geçerliliği test edildi. Bu çalışmada, çalışma
alanından toplanan su örneklerindeki ortalama aktivite konsantrasyonları ²²⁶Ra, ²³²Th ve ²³⁸U çekirdekleri için sırasıyla
1.439 Bql^-1, 4.508 Bql^-1 ve
14.682 Bql^-1 dir. Çalışma alanın karakteristikleri de belirlendi ve
²²⁶Ra, ²³²Th
ve ²³⁸U radyoaktif
çekirdek konsantrasyonlarının tahmini ve modellemesi için Yapay Sinir Ağları
(YSA) kullanıldı. Elde edilen sonuçlara ait ortalama kare hatalar 1,5 tan
azdır. Korelasyon katsayısının da +1 e yakın çıkması modelin geçerliliğinin bu
çalışma için uygunluğunu göstermektedir.

Anahtar Kelimeler

Doğal Radyoaktivite, Taşınım, Dağılım, Radyum, Toryum, Uranyum, Modelleme, Tahmin, Yapay Sinir Ağı

Forecasting of Ra(226), Th(232) and U(238) Concentrations using Artificial Neural Networks (ANNs)

Öz

Identification and modeling of radioactive
concentrations in a region is very important for the region in terms of
radiological hazards. Artificial Neural Network (ANN) can successfully model
large systems. The validity of the
model was tested by entering the data of the proposed ANN model that had never
been entered into the system. In this research, average activity
concentrations of ²²⁶Ra, ²³²Th
and ²³⁸U in the water samples collected from the lake are 1.439 Bql^-1, 4.508 Bql^-1 and
14.682   Bql^-1, respectively. The
characteristics of the study area are also determined with the spatial maps and
ANNs are used to prediction and modeling of the radionuclides. The mean square
errors for the obtained results are less than 1.5%. The correlation coefficient close to +1 indicates
the validity of the model for this study.

Anahtar Kelimeler

Natural Radioactivity, Transport, Distribution, Radium, Thorium, Uranium, Modelling, Prediction, Artificial Neural Network

Kaynakça

• [1]. IAEA, Naturally Occurring Radioactive Material, 2007.
• [2]. UNSCEAR, United Nations Scientific Committee on the Effect of Atomic Radiation, 1988.
• [3]. Külahcı F. Spatiotemporal (four-dimensional) modeling and simulation of uranium (238) in Hazar Lake (Turkey) water, Environ Earth Sci. 75 (2016) 452.
• [4]. Krisnaswami S., Graustein W.C., Turekian K.K, Dowd J.F., Radium, Thorium and Radioactive Lead Isotopes In Groundwaters - Application To The Insitu Determination Of Adsorption-Desorption Rate Constants And Retardation Factors, Water Resour. Res., 18 (1982) 1663-1675.
• [5]. Závodská L., Kosorínová E., Ščerbáková L., Lesný J. Environmental Chemistry of Uranium. Hej, Env-081221-A., (2008) 1-19.
• [6]. Yeşilkanat C.M., Kobya Y., Determination and mapping the spatial distribution of radioactivity of natural spring water in the Eastern Black Sea Region by using artificial neural network method Environ. Monit. Assess., (2015) 187-589.
• [7]. Medhat M.E. Artificial intelligence methods applied for quantitative analysis of natural radioactive sources. Annals of Nuclear Energy, 45 (2015)73-79.
• [8]. Akkoyun S., Bayram T., Yildiz N. Estimations of Radiation Yields for Electrons in Various Absorbing Materials, Cumhuriyet Science Journal, 37 (2016) 65-65.
• [9]. Castin N., Malerba L., Chaouadi R. Prediction of radiation induced hardening of reactor pressure vessel steels using artificial neural networks. J. Nucl. Mater., 408 (2011) 30-39.
• [10]. Külahcı F., İnceöz M., Doğru M., Aksoy E., Baykara O. Artificial neural network model for earthquake prediction with radon monitoring,A Radiation and Isotopes, 67 (2009) 212-219.
• [11]. Niksarlioglu S., Kulahci F. An Artificial Neural Network Model for Earthquake Prediction and Relations between Environmental Parameters and Earthquakes, WAS Engineering and Technology, 74 (2013) 984-987.
• [12]. Negarestani A., Setayeshi S., Ghannadi-Maragheh M., Akashe B. Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network. App. Radiation Isot., 58 (2003) 269-273.
• [13]. Yeşilkanat C.M., Kobya Y., Taşkın H., Çevik U. Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neuralnetworks and fuzzy logic methods, Journal of Environmental Radioactivity, (2017) 78-93.
• [14]. Külahcı F., Doğru M. Iso-Radioactivity Curves of the water of the Hazar Lake, Elazig, Turkey, J. Radioanal. Nucl. Chem. , 260 (2004) 557-562.
• [15]. Faussett L., Fundamentals of Neural Networks Architectures, 1994; 461 pp.
• [16]. Fernandez C., Soria E., Martin J.D. Serrano, A.J., Neural networks for animal science applications: Two case studies, Expert. Syst. Appl., 31 (2006) 444-450.
• [17]. Tsoukalas L.H., Uhring R.E. Fuzzy and Neural Approaches in Engineering, 1997; 600 pp.
• [18]. Özger M., Şen Z., Triple diagram method for the prediction of wave height and period, Ocean engineering, 34 (2007) 1060-1068.