Science Faculty, Physics Department, Dokuz Eylul University, 35390, İzmir, Turkey.
Environ Monit Assess. 2024 Feb 28;196(3):315. doi: 10.1007/s10661-024-12459-8.
The estimation of exposures to humans from the various sources of radiation is important. Radiation hazard indices are computed using procedures described in the literature for evaluating the combined effects of the activity concentrations of primordial radionuclides, namely, U, Th, and K. The computed indices are then compared to the allowed limits defined by International Radiation Protection Organizations to determine any radiation hazard associated with the geological materials. In this paper, four distinct radial basis function artificial neural network (RBF-ANN) models were developed to predict radiation hazard indices, namely, external gamma dose rates, annual effective dose, radium equivalent activity, and external hazard index. To make RBF-ANN models, 348 different geological materials' gamma spectrometry data were acquired from the literature. Radiation hazards indices predicted from each RBF-ANN model were compared to the radiation hazards calculated using gamma spectrum analysis. The predicted hazard indices values of each RBF-ANN model were found to precisely align with the calculated values. To validate the accuracy and the adaptability of each RBF-ANN model, statistical tests (determination coefficient (R), relative absolute error (RAE), root mean square error (RMSE), Nash-Sutcliffe Efficiency (NSE)), and significance tests (F-test and Student's t-test) were performed to analyze the relationship between calculated and predicted hazard indices. Low RAE and RMSE values as well as high R, NSE, and p-values greater than 0.95, 0.71, and 0.05, respectively, were found for RBF-ANN models. The statistical tests' results show that all RBF-ANN models created exhibit precise performance, indicating their applicability and efficiency in forecasting the radiation hazard indices of geological materials. All the RBF-ANN models can be used to predict radiation hazard indices of geological materials quite efficiently, according to the performance level attained.
对来自各种辐射源的人类暴露进行估计很重要。辐射危害指数是使用文献中描述的程序计算得出的，用于评估原始放射性核素（即 U、Th 和 K）的活度浓度的综合影响。然后将计算出的指数与国际辐射防护组织定义的允许限值进行比较，以确定与地质材料相关的任何辐射危害。在本文中，开发了四个不同的径向基函数人工神经网络 (RBF-ANN) 模型来预测辐射危害指数，即外部伽马剂量率、年有效剂量、镭当量活度和外照射指数。为了构建 RBF-ANN 模型，从文献中获取了 348 种不同地质材料的伽马能谱数据。使用伽马能谱分析计算得到的辐射危害与每个 RBF-ANN 模型预测的危害指数进行了比较。发现每个 RBF-ANN 模型预测的危害指数值与计算值非常吻合。为了验证每个 RBF-ANN 模型的准确性和适应性，进行了统计检验（决定系数 (R)、相对绝对误差 (RAE)、均方根误差 (RMSE)、纳什-苏特克里夫效率 (NSE)）和显著性检验（F 检验和学生 t 检验），以分析计算和预测的危害指数之间的关系。结果发现，对于 RBF-ANN 模型，RAE 和 RMSE 值较低，R、NSE 和 p 值较高，分别大于 0.95、0.71 和 0.05。统计检验结果表明，所创建的所有 RBF-ANN 模型均表现出精确的性能，表明它们在预测地质材料的辐射危害指数方面具有适用性和效率。根据达到的性能水平，所有 RBF-ANN 模型都可以非常有效地用于预测地质材料的辐射危害指数。
