Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.

中文翻译：

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基于遥感和基于GIS的机器学习技术的地下水位图

对于农村人口来说，充足的地下水开发至关重要，因为地下水是饮用水和农业用水的重要来源。在这项研究中，基于决策树的机器学习算法的集成模型与地理信息系统（GIS）一起用于绘制和测试韩国养平郡的地下水产量潜力。从遥感数据中得出的地下水控制因子用于制图，包括九个地形因子，两个水文因子，森林类型，土壤材料，土地利用和两个地质因子。总共选择了53个具有比容（SPC）数据和透射率（T）数据的井位，并将它们随机分为两类，分别用于模型训练（70％）和测试（30％）。首先，计算SPC和T的频率比（FR），然后应用了机器学习模型的增强分类树（BCT）方法。此外，采用了集成模型FR-BCT来生成和比较地下水位图。使用接收器工作特性（ROC）方法评估模型性能。为了测试模型，计算了ROC曲线下的面积；BPC和FR-BCT模型的SPC预测数据集曲线分别显示80.48％和87.75％的值。对于BCT和FR-BCT模型，来自T的准确率分别为72.27％和81.49％。BCT和FR-BCT模型均测量了各个地下水控制因素的贡献，这表明土壤是影响最大的因素。这项研究中使用的机器学习技术显示了在数据相对匮乏的地区有效的地下水潜力建模。通过确定地下水潜力高的地区，本研究的结果可用于地下水资源的可持续发展。