This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; U and Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for U and Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater.

中文翻译：

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通过解决数据不平衡问题提高地下水中高浓度天然放射性物质发生概率原位虚拟监测系统性能

本文提出了两种数据驱动的虚拟传感器，根据原位地下水质量监测数据和地质信息来估计地下水中自然产生的放射性物质（NORM；U和Rn）高浓度出现概率的时间序列。根据实际现场地下水质量数据、岩石类型和含水层深度，应用随机森林来估计 NORM 浓度。此外，本研究提出了三种数据采样技术（即欠采样、合成少数过采样和复杂采样）来提高模型的适用性和准确性。使用从韩国 201 个地点获取的实际数据对开发的模型进行了验证。U 和 Rn 的模型显示估计准确度分别为 85% 和 80%；具有过采样的模型表现出更好的性能。所有结果都验证了所开发模型作为虚拟传感器的有用性，可提供有关地下水中 NORM 现场存在的即时信息。