The rapid decline of groundwater levels (GWL) due to pervasive groundwater abstraction in the densely populated (~1 billion) Indus-Ganges-Brahmaputra-Meghna (IGBM) transboundary river basins of South Asia, necessitates a robust framework of prediction and understanding. While few localized studies exist, three-dimensional regional-scale characterization of GWL prediction is yet to be implemented. Here, ‘support vector machine’, a machine-learning-based method, is applied to data from the Gravity Recovery and Climate Experiment (GRACE) and data on land-surface-model-based groundwater storage and meteorological variables, to predict the GWL anomaly (GWLA) in the IGBM. The study has three main objectives, (1) to understand the spatial (observation well locations) and subsurface (shallow vs. deep observation wells) variability in prediction results for in-situ GWLA data for a large number of observation wells (n = 4,791); (2) to determine its relationship with groundwater abstraction, and; (3) to outline the advantages and limitations of using GRACE data for predicting GWLAs. The findings, based on individual observation well results, suggest significant prediction efficiency (median statistics: r > 0.71, NSE > 0.70; p < 0.05) in most of the IGBM; however, the study identifies hotspots, mostly in the agriculture-intensive regions, having relatively poor model performance. Further analysis of the subsurface depth-wise prediction statistics reveals that the significant dominance of pumping in the deeper depths of the aquifer is linked to the relatively poor model performance for the deep observation wells (screen depth > 35 m) compared with the shallow observation wells (screen depth < 35 m), thus, highlighting the limitation of GRACE in representing spatial and depth-dependent local-scale pumping.

由于南亚人口稠密（约10亿）的印度河-甘格斯-布拉马普特拉-梅格纳河（IGBM）跨界河流盆地中普遍存在地下水抽取，地下水位（GWL）迅速下降，因此需要一个强有力的预测和理解框架。尽管很少有本地化研究，但GWL预测的三维区域尺度表征尚待实现。在这里，“支持向量机”（一种基于机器学习的方法）被应用于重力恢复和气候实验（GRACE）的数据以及基于地表模型的地下水存储和气象变量的数据，以预测GWL IGBM中的异常（GWLA）。该研究的三个主要目标是：（1）了解空间（观测井的位置）和地下（浅层与浅层）。n  = 4,791）；（2）确定其与地下水抽取的关系，以及；（3）概述了使用GRACE数据预测GWLA的优点和局限性。根据个别观察井的结果，这些发现表明了显着的预测效率（中位数统计：r  > 0.71，NSE> 0.70；p <0.05）在大多数IGBM中；但是，该研究确定的热点（主要在农业密集型地区）的模型性能相对较差。对地下深度预测统计数据的进一步分析表明，与浅层观测井相比，深层观测井（筛网深度> 35 m）在深层含水层中抽水的显着优势与较差的模型性能有关。 （屏幕深度<35 m），因此，突出显示了GRACE在表示空间和深度相关的局部尺度泵送方面的局限性。