Enhancing groundwater predictions by incorporating response lag effects in machine learning models - Yang, F.; Zhang, X.; Yang, J.; Zhang, J.; Dai, Q.; Zhu, S. (IWA Publishing, 2025)
Groundwater is essential for water resources but faces over-extraction and supply-demand imbalance. Precisely comprehending alterations in groundwater is crucial for sustainable development. Groundwater levels demonstrate a delayed reaction to meteorological circumstances, frequently neglected in current research, diminishing predictive accuracy. This study investigates the lag effect of precipitation and evapotranspiration on groundwater forecasting in Hebei Province, China. We performed a lag analysis utilizing long-term data to ascertain correlations between groundwater levels and climatic variables. Two groundwater prediction models using the random forest algorithm were developed, one incorporating the lag effect and the other excluding it. The Shapley Additive exPlanations (SHAP) method assessed the significance of each element and its influence on model variations. Research reveals a notable lag in groundwater response: shallow groundwater reacts to precipitation after 4.55 months and to evapotranspiration after 9.21 months; deep groundwater responds after 5.91 and 9.63 months, respectively. The inclusion of the lag effect resulted in higher accuracy of the model, with an average reduction of 35.7% in MAE and 18.20% in RMSE. The improved model more accurately captured the influence of meteorological factors on groundwater levels, potentially providing more scientific decision support for the rational allocation and sustainable use of water resources.