Spatial modeling of radon potential mapping using deep learning algorithms. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: Panahi, Mahdi; Yariyan, Peyman; Rezaie, Fatemeh; Kim, Sung Won; Sharifi, Alireza; Alesheikh, Ali Asghar; Lee, Jongchun; Lee, Jungsub; Kim, Seonhong; Yoo, Juhee; Lee, Saro
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 9560
• Page End: 9582
• Publication Date: 2022-12-13
• Subjects: Radon potential mapping -- deep learning models -- CNN -- RNN -- LSTM; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.2022011
• Languages: English
• ISSNs: 1010-6049

Abstract

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 [K2 O], and ferric oxide [Fe2 O3 ] concentrations; effective soil depth; topsoil texture; and soil drainage). The models were validated based on the area under the receiver operating curve ( AUC ), mean squared error ( MSE ), root mean square error ( RMSE ), and standard deviation ( StD ). The CNN model with AUC values of 0.906 and 0.905 in the learning and testing stages, respectively, is introduced as the optimal model. The lowest StD, MSE, and 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.