Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models

Fig. Schematic representation of RFSI model process for predicting groundwater level at high spatial resolution. • Random Forest for Spatial Interpolation (RFSI) used to generate 1 km 2 groundwater level estimates at monthly intervals. • Observations and distances to nearest monitoring points were used to estimate groundwater levels at unmonitored locations. • High resolution (1 km 2 ) monthly groundwater level maps generated for the period 2001-2022. • The RFSI model demonstrated significantly higher computational efficiency. Groundwater is an important resource for sustainable crop growth and agricultural productivity, and groundwater level (GWL) fluctuations directly influence agroecosystems. However, GWL data for most regions is unavailable because of spatial scarcity and discontinuous groundwater observations. In recent studies, machine learning techniques have proven to be a useful tool for producing GWL estimates, but these techniques may not necessarily take into account distances and observations from the nearest points to unmonitored sites as covariates. In this study, we explored the potential of these additional spatial covariates by implementing Random Forest for Spatial Interpolation (RFSI) to generate high-resolution (1 km 2 ) GWL estimates on a monthly scale. To evaluate the effectiveness of the RFSI model, we performed a thorough comparison of the RFSI model with a conventional Random Forest (RF) model, a Random Forest for spatial data (RFsp) and a Support Vector Machine (SVM). The framework was implemented using GWL observation data for the period 2001 to 2022 in Brandenburg, Germany. The RFSI model exhibited the highest predictive accuracy for GWL predictions at testing points, achieving an R 2 value of 0.86, surpassing the performance of RF, RFsp and SVM with R 2 values of 0.75, 0.72 and 0.67, respectively. Incorporating nearby-well information substantially improved 1 km GWL predictions: RFSI achieved the lowest RMSE (3.77 m), as compared to RF (4.85 m), RFsp (5.04 m), and SVM (5.59 m). Notably, among the models tested, RF, RFsp, and SVM showed notable inaccuracies, substantially underestimating groundwater levels (GWL) in deeper wells (>10 m) and overestimating levels in shallow wells (1–10 m). In contrast, RFSI performed better, with smaller overestimations (up to ∼1.1 m) and less severe underestimations (up to ∼−3.9 m). Furthermore, the RFSI model demonstrated significantly higher computational efficiency compared to RFsp and SVM, particularly for large training datasets and high-resolution prediction mapping.