Spatial modeling of radon potential mapping using deep learning algorithms

Abstract

Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) is presented to predict radon potential in the northwestern part of Gangwon Province, South Korea. The used data in this study are in two sets of dependent variables (measured soil gas radon concentrations) and independent variables (radon conditioning factors: lithology; distance from lineament; mean soil calcium oxide [Cao], potassium oxide [K₂O], and ferric oxide [Fe₂O₃] concentrations; effective soil depth; topsoil texture; and soil drainage). The models were validated based on the area under the receiver operating curve ($\mathrm{AUC}$), mean squared error ($\mathrm{MSE}$), root mean square error ($\mathrm{RMSE}$), and standard deviation ($\mathrm{StD}$). The CNN model with $\mathrm{AUC}$ values of 0.906 and 0.905 in the learning and testing stages, respectively, is introduced as the optimal model. The lowest $\mathrm{StD},$ $\mathrm{MSE},$ and $\mathrm{RMSE}$ values were from the CNN, LSTM, and RNN models, respectively. Our results show that the use of deep learning models to generate radon potential maps is promising and reliable.

Keywords:

• Radon potential mapping
• deep learning models
• CNN
• RNN
• LSTM

Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT. Furthermore, this work was supported by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2013-02-01-001).

Disclosure statement

No potential conflict of interest was reported by the author(s).