Fatih KULAHCI, Sevim BILICI, Mirac KAMISLIOGLU, Ahmet BILICI

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

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

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

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

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