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Spatial modeling of radon potential mapping using deep learning algorithms

DATE2022-10-31 HIT93

Spatial modeling of radon potential mapping using deep learning algorithms

Geocarto International


Authors : Mahdi Panahi, Peyman Yariyan, Fatemeh Rezaie, Sung Won Kim, Alireza Sharifi, Ali Asghar Alesheikh, Jongchun Lee, Jungsub Lee, Seonhong Kim, Juhee Yoo & Saro Lee

ISSN : 1010-6049

  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 (AUC AUC), mean squared error (MSE MSE), root mean square error (RMSE RMSE), and
standard deviation (StD StD). The CNN model with AUC AUC values of 0.906 and 0.905 in the learning
and testing stages, respectively, is introduced as the optimal model. The lowest StD, StD, MSE, MSE, and
RMSE 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.

Keyword : Radon potential mapping, deep learning models, CNN, RNN, LSTM

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