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Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points: Groundwater potential mapping was carried out at the Central Highlands of VietnamHybrid Machine learning Models based on Naïve Bayes Tree were developed and usedResults showed that the proposal models are potential and accurate tools [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) plays a very important role in China's economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB. [ABSTRACT FROM AUTHOR]