Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

检查地下水资源状况和干旱的影响对于有效的水资源管理很有价值。如今，机器学习 (ML) 模型被认为是时间序列预测的有用工具之一。在本研究中，使用 MODFLOW 对伊朗西北部最重要的含水层之一的地下水状况进行了调查，然后利用多元自适应回归样条 (MARS) 和最小二乘支持向量回归 (LSSVR) 估算地下水资源指数 (GRI) 2001年至2019年期间。气象和水文干旱指标以及降水量和流量被用作预测的输入变量。模拟结果表明，由于含水层抽取量大于补给量，导致地下水位下降。此外，结果表明，地表水与地下水资源之间的相互作用有限，主要是由河流流量减少和含水层地下水位下降引起的。两种 ML 模型在使用地下水流量、径流干旱指数、标准化降水指数和径流作为输入变量的 GRI 估计中均表现良好。 RMSE、MAE 和 NSE 误差评估标准分别为 0.37、− 0.19 和 0.83 的 MARS 模型的性能略好于 RMSE、MAE 和 NSE 分别为 0.48、− 0.06 和 0.80 的 LSSVR。 。研究结果揭示了这两种模型在预测干旱指标方面的适当性能，强调了在水文和干旱预测问题中使用机器学习模型的必要性。