Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea; Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
Division of Science Education, Kangwon National University, 1, Gangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea.
Environ Int. 2023 Jan;171:107724. doi: 10.1016/j.envint.2022.107724. Epub 2022 Dec 30.
Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations.
室内氡及其子体的长期吸入会对居住者的健康造成严重问题；因此，氡含量高的地区的房屋开发引起了地方当局的极大关注。必须确定具有高氡暴露潜力的区域，以成功实施具有成本效益的氡缓解计划，或防止建造不安全的建筑物。在这项研究中，使用基于当地土壤特性、地质成因、地球化学以及地形因素的分组数据处理（GMDH）算法，生成了韩国忠清南道的室内氡潜力图。为了最优地调整 GMDH 的超参数并提高建模氡分布的预测精度，将 GMDH 模型与两种元启发式优化算法（蝙蝠（BA）和布谷鸟搜索（COA）算法）集成在一起。使用接收器工作特征（ROC）曲线下的面积（AUC）、均方误差（MSE）、均方根误差（RMSE）和标准差（StD）来量化模型的拟合优度和预测性能。结果表明，在训练（AUC=0.852，MSE=0.058，RMSE=0.242，StD=0.242）和测试（AUC=0.844，MSE=0.060，RMSE=0.246，StD=0.0242）阶段，GMDH-COA 模型的性能均优于其他模型。此外，使用元启发式优化算法提高了 GMDH 的预测能力。GMDH-COA 模型表明，忠清南道总面积的约 7%属于高氡易发性地区。使用信息增益比方法来评估所考虑因素的预测能力。不出所料，土壤特性和当地地质对氡潜力水平的空间分布有很大影响。本研究生成的氡潜力图代表了识别预计由于建筑物基础下岩石和衍生土壤中天然放射性同位素浓度较高而导致大量住宅建筑经历显著氡水平的地区的第一阶段。生成的地图有助于地方当局更明智地制定城市规划，以减少氡浓度。
