In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.

在本研究中，AdaBoost、MultiBoost 和 RealAdaBoost 方法与二次判别分析方法相结合，开发了三种新的基于 GIS 的机器学习集成模型，即 ABQDA、MBQDA 和 RABQDA，用于越南 Dak Nong 省的地下水潜力测绘. 本研究总共使用了 227 口地下水井和 12 个调节因子（入渗、降雨、河流密度、地形湿度指数、泥沙输运指数、河流功率指数、高程、坡向、曲率、坡度、土壤和土地利用）。使用接受者操作特征曲线下面积 AUC (AUC) 和其他几个性能指标评估模型的性能。结果表明，达到AUC=0的ABQDA模型。741 在生成 Dak Nong 省地下水潜力的准确地图方面优于其他模型。此处生成的模型和潜在地图可以帮助政策制定者和水资源管理者保持对这些重要资源的最佳利用。