Feasibility study of using the artificial neural network modeling for estimation the radiological levels for the environmental samples

An artificial neural network (ANN) code is carried out to estimate the radiological concentration of environmental samples, clay, and sand. The ANN modeling has been calibrated and validated with the measuring data acquired from an HPGe detector. Radioactivity was determined using a gamma spectrometric detector (HPGe) for the collection of clay and sand samples from Al-Tod, Luxor, Egypt. The radiological levels of the natural radioactive series of 226Ra, 232Th, and 40K-isotope have been estimated for the sand and clay samples. The average specific activity of 226Ra, 232Th, and 40K for clay-samples were 14.43 ± 0.5 Bq/kg, 15.8 ± 0.6 Bq/kg, and 273.2 ± 6.1 Bq/kg, respectively, where it for sand-samples were 12.4 ± 0.46 Bq/kg, 13.4 ± 0.5 Bq/kg, and 215.4 ± 15.11 Bq/kg, respectively. The feasibility of the ANN model has been studied by comparing its outcome data with the measured one. However, a robust correlation is observed for the radiological levels achieved by the ANN model and for the measured one in an error of around 5 percent. Therefore, besides measurement techniques, the ANN model could be a promising candidate for estimating and predicting the radiological levels from environmental samples. However, the use of the ANN model will reduce the measurement time and cost over many samples.