Despite numerous studies in recent times, there is no consensus on the primary drivers for groundwater storage (GWS) changes over India. Thus, predicting future groundwater level trends seems remote. In this context, using Gravity Recovery and Climate Experiment (GRACE)-derived GWS, WaterGap model-based groundwater recharge (GWR), and groundwater withdrawal (GWW), we show that GWW exhibits a stronger dominance than GWR on GWS change over India. Furthermore, we developed feed-forward neural network (FNN), recurrent neural network (RNN), and deep learning-based long short-term memory network (LSTM) models using multidepth in situ observations from a dense network of monitoring wells (n = 5367, 1996–2018), to simulate and forecast groundwater levels (GWL) in India. The result demonstrates the better performance of LSTM (>84% of observation wells showing r > 0.6, RMSE_n < 0.7) across India, outperforming both FNN and RNN in the testing period of 5 years (2014–2018). Our estimates also reveal that besides the prevailing long-term (1996–2018) statistically significant (p < 0.1) declining GWL trends in northwest India and the Ganges river basin, higher declining trends will potentially be observed in parts of north-central and south India in the forecasting period of 5 years (2019–2023). We envisage that the forecasting approach presented in the study can contribute toward an improved urban–rural drinking water supply and sustainable crop production for 1.3 billion people in India.

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基于深度学习的印度地下水位趋势预测：对作物生产和饮用水供应的影响

尽管最近进行了大量研究，但对于印度地下水储存 (GWS) 变化的主要驱动因素尚未达成共识。因此，预测未来的地下水位趋势似乎很遥远。在这种情况下，我们使用重力恢复和气候实验 (GRACE) 衍生的 GWS、基于 WaterGap 模型的地下水补给 (GWR) 和地下水抽取 (GWW)，表明 GWW 在印度的 GWS 变化上表现出比 GWR 更强的优势。此外，我们开发了前馈神经网络 (FNN)、循环神经网络 (RNN) 和基于深度学习的长短期记忆网络 (LSTM) 模型，这些模型使用来自监测井 ( n)的密集网络的多深度原位观测。= 5367, 1996–2018)，以模拟和预测印度的地下水位 (GWL)。结果表明，LSTM（>84% 的观测井显示r > 0.6，RMSE _n < 0.7）在整个印度的性能更好，在 5 年（2014-2018 年）的测试期间优于 FNN 和 RNN。我们的估计还表明，除了普遍存在的长期（1996-2018）统计显着（p< 0.1) 印度西北部和恒河流域的 GWL 趋势下降，在 5 年的预测期内（2019-2023 年），印度中北部和南部部分地区可能会观察到更高的下降趋势。我们预计，研究中提出的预测方法有助于改善印度 13 亿人的城乡饮用水供应和可持续作物生产。