Prediction of the activity concentrations of Th-232, U-238 and K-40 in geological materials using radial basis function neural network

ERZİN S., YAPRAK G.

JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY, cilt.331, sa.9, ss.3525-3533, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 331 Sayı: 9
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s10967-022-08438-3
  • Dergi Adı: JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Analytical Abstracts, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, EMBASE, Food Science & Technology Abstracts, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3525-3533
  • Anahtar Kelimeler: Gamma spectrometry, NaI(Tl) detector, Neural networks, Radial basis function, METHODOLOGY, SOILS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

In this paper, three individual models and one generalized radial basis function neural network (RBFNN) model were developed for the prediction of the activity concentrations of primordial radionuclides, namely, Th-232, U-238 and K-40. To achieve this, gamma spectrometry measurements of 126 different geological materials were used in the development of the RBFNN models. The results indicated that individual and generalized RBFNN models are quite efficient in predicting the activity concentrations of Th-232, U-238 and K-40 of geological materials.