Evaluating machine learning models in predicting GRI drought indicators (case study: Ajabshir area)

Abstract

Abstract 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.

Keywords

• Groundwater management
• Machine learning
• Streamflow drought index (SDI)
• Groundwater resource index (GRI)
• MODFLOW