Investigating the Relationship between Soil Gas Radon and Soil Permeability by Using Artificial Neural Networks

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This study aims to explore the relationship between soil gas radon concentration (CRn) and soil permeability (k). To accomplish this, a single linear regression analysis (SLRA) model and an artificial neural network (ANN) model were built from 142 soil gas CRn and k measurements collected from the literature. When soil gas CRn values predicted by both models were compared with those measured, the ANN model outperformed the SLRA model. Furthermore, several performance metrics, including correlation coefficient, root mean square error, relative absolute error, and mean absolute error were determined to examine the prediction capabilities of SLRA and ANN models. The metrics obtained demonstrated that the ANN model exhibited superior performance to the SLRA model, thereby showing the accuracy and applicability of the ANN model for forecasting soil gas CRn values. The study's findings indicated that the developed ANN model may be utilized to forecast soil gas CRn values based on soil k values.