School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China.
School of Earth Science and Engineering, Hohai University, Nanjing, 210098, China.
J Environ Manage. 2023 Nov 1;345:118817. doi: 10.1016/j.jenvman.2023.118817. Epub 2023 Aug 17.
A new method relying on machine learning and resistivity to predict concentrations of petroleum hydrocarbon pollution in soil was proposed as a means of investigation and monitoring. Currently, determining pollutant concentrations in soil is primarily achieved through costly sampling and testing of numerous borehole samples, which carries the risk of further contamination by penetrating the aquifer. Additionally, conventional petroleum hydrocarbon geophysical surveys struggle to establish a correlation between survey results and pollutant concentration. To overcome these limitations, three machine learning models (KNN, RF, and XGBOOST) were combined with the geoelectrical method to predict petroleum hydrocarbon concentrations in the source area. The results demonstrate that the resistivity-based prediction method utilizing machine learning is effective, as validated by R-squared values of 0.91 and 0.94 for the test and validation sets, respectively, and a root mean squared error of 0.19. Furthermore, this study confirmed the feasibility of the approach using actual site data, along with a discussion of its advantages and limitations, establishing it as an inexpensive option to investigate and monitor changes in petroleum hydrocarbon concentration in soil.
提出了一种新的方法，该方法依赖于机器学习和电阻率来预测土壤中石油烃污染的浓度，作为调查和监测的手段。目前，确定土壤中污染物浓度主要是通过对大量钻孔样本进行昂贵的采样和测试来实现的，这存在着穿透含水层进一步污染的风险。此外，传统的石油烃地球物理调查难以在调查结果与污染物浓度之间建立相关性。为了克服这些限制，将三种机器学习模型（KNN、RF 和 XGBOOST）与电阻率法相结合，以预测源区中的石油烃浓度。结果表明，基于电阻率的机器学习预测方法是有效的，验证集的 R 方值分别为 0.91 和 0.94，均方根误差为 0.19。此外，本研究还使用实际现场数据证实了该方法的可行性，并讨论了其优缺点，将其作为一种低成本的调查和监测土壤中石油烃浓度变化的方法。
