Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods
Yükleniyor...
Dosyalar
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier SCI LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure.
Açıklama
Anahtar Kelimeler
Gamma dose rate, Artificial neural network, Fuzzy logic, Radiological risk maps, Interpolation
Kaynak
Journal of Environmental Radioactivity
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
175
Sayı
Künye
Yeşilkanat, C. M., Kobya, Y., Taşkın, H., & Çevik, U. (2017). Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods. Journal of Environmental Radioactivity, 175, 78-93.
