Exposure to indoor radon, with no safe level, has been reported to bear the possible radiological risk to humans. The indoor radon level of a total of one hundred and thirty-two offices and sixty classrooms of tertiary institutions within different lithology and at varied meteorological values in southwestern Nigeria was measured using Electret Passive Environmental Radon Monitor (E-PERM). The meteorological parameters were obtained from the National Aeronautics and Space Administration (NASA) database. MATLAB scripts of code were used to develop the Artificial Neural Network (ANN) model. The measured parameters were subjected to both descriptive and inferential statistics. The highest mean radon concentration was observed in offices built on granitic bedrock with a value of 64.3 ± 1.7 Bq.m-3 while the lowest was observed in alluvium bedrock with a value of 52.5 ± 1.4 Bq.m-3. To enhance prediction involving erratic parametric patterns, the measured data were subjected to an optimized Artificial Neural Network architecture training, validation, and testing, leading to a model determined to have a Nash-Sutcliffe efficiency coefficient value of 0.997, Average Absolute Relative Error of 0.0115, and Mean Squared Error of 0.07. The predicted result was compared favorably with the measured data with 0.054 Average Validation Error, 0.027 Mean Absolute Error 3.64 Mean Absolute Percentage Error, and 83.7% Goodness-of-Prediction values. About 21.4% of the values were found to be higher than the 100 Bq.m-3 limits specified by the World Health Organization. Measured radon concentration and predicted ANN data as obtained in this work, being novel in this study area is useful for immediate assessment of the level of risk associated with radon exposure as well as for future predictions. The ANN developed is effective and efficient in predicting indoor radon concentration.
Keywords: Artificial Neural Network; E-PERM; Indoor radon; Radiological risk; WHO.
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