Optimization of Artificial Neural Networks for Predicting the Radiological Risks of Thermal Waters in Türkiye
Source abstract
Abstract
In this study, the prediction of four radiological risk parameters of thermal waters in Türkiye (dose contribution (DE) from radon release in thermal water to air for workers and visitors, the annual effective dose from radon ingestion (Ding) and the annual effective dose to the stomach from radon ingestion (Dsto)) from three physicochemical properties of thermal waters (electrical conductivity (EC), pH and temperature (T)) was investigated using multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). To achieve this, two separate MLPANN and RBFANN models were constructed using data from the literature. The MLPANN and RBFANN models were verified using performance metrics (relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), and ratio of RMSE to data standard deviation (RSR)). The comparison of performance metrics shows that MLPANN models achieved approximately 54% lower error metrics than RBF models. The performance of the developed models was further examined using rank analysis, Taylor and Scaled Percentage Error (SPE) plots. Rank analysis and Taylor and SPE graphs showed that MLPANN models predicted the values of four radiological risk parameters of thermal waters more correctly than RBFANN models. This study demonstrates that MLPANNs significantly outperformed RBFANNs in predicting the radiological risks of thermal waters in Türkiye.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Fusion Techniques · Brain Tumor Detection and Classification · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Physics Department, Science Faculty, Dokuz Eylul University, 35390 İzmir, Türkiye
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