The accurate acquisition of soil water content is a fundamental cornerstone of research into hydrological processes and agricultural engineering. Electrical Resistivity Tomography (ERT) has been validated for hydrological studies and soil monitoring. The establishment of a quantitative relationship between ERT resistivity data and soil water content is usually based on rock physics models. However, the applicability of such models in complex environments and the acquisition of the relevant parameters pose a certain challenge. In addition, the spatial resolution of ERT limits its application in soil moisture assessment. Therefore, a machine learning-based approach is proposed in this study to determine the quantitative relationship between resistivity and soil water content. We investigate the integration of three machine learning models (KNN, RF, XGBOOST) with ERT to predict the water content of clay soils. The results show that the RF model achieves an R2 of 0.92 with an RMSE of 0.41. To improve the ERT resolution, a new data collection method (MRU) is introduced in this study by increasing the density of ERT data collection. A comparative analysis is conducted between traditional ERT data collection methods and the MRU approach in terms of soil water content prediction accuracy. The results show that the MRU method of data collection improves the accuracy of soil water content prediction by an average of 57% compared to traditional methods. This study confirms the feasibility of using machine learning models to establish mappings between resistance and water content and shows that the MRU data collection method for ERT effectively improves the accuracy of predicting soil water content. These results provide a new perspective for hydrological process research and agricultural monitoring technology.

中文翻译：

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使用增强型高分辨率 ERT 通过机器学习量化土壤含水量


土壤含水量的准确获取是水文过程和农业工程研究的基本基石。电阻率断层扫描 (ERT) 已被验证可用于水文研究和土壤监测。 ERT电阻率数据与土壤含水量之间定量关系的建立通常基于岩石物理模型。然而，此类模型在复杂环境下的适用性以及相关参数的获取提出了一定的挑战。此外，ERT的空间分辨率限制了其在土壤水分评估中的应用。因此，本研究提出了一种基于机器学习的方法来确定电阻率与土壤含水量之间的定量关系。我们研究了三种机器学习模型（KNN、RF、XGBOOST）与 ERT 的集成来预测粘土的含水量。结果表明，RF 模型的 R2 为 0.92，RMSE 为 0.41。为了提高 ERT 分辨率，本研究引入了一种新的数据收集方法（MRU），通过增加 ERT 数据收集的密度。对传统ERT数据采集方法与MRU方法在土壤含水量预测精度方面进行了比较分析。结果表明，数据采集的MRU方法比传统方法平均提高了土壤含水量预测的精度57%。本研究证实了利用机器学习模型建立阻力与含水量之间映射的可行性，并表明ERT的MRU数据采集方法有效提高了土壤含水量预测的准确性。 这些成果为水文过程研究和农业监测技术提供了新的视角。