Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms
Authors : Fatemeh Rezaie, Mahdi Panahi, Jongchun Lee, Jungsub Lee, Seonhong Kim, Juhee Yoo, Saro Lee
ISSN : 0269-7491
The adverse health effects associated with the inhalation and ingestion of naturally occurring
radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas.
This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and
machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN).
Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density,
lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe2O3), and lead (Pb) concentrations,
were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into
training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high,
moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was
classified as very high or high risk. Finally, the radon potential maps were validated using the area under the
receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR
method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets,
respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best
predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon,
and could inform the development of new residential areas. Radon screening is important to reduce public exposure
to high levels of naturally occurring radiation.
Keyword : Radon potential map, Frequency ratio, Convolutional neural network, GIS, Jangsu-gun
Link : https://doi.org/10.1016/j.envpol.2021.118385