Groundwater level predictive monitoring is necessary to address accelerated aquifer depletion and ensure sustainable management under increasing climatic and anthropogenic pressures. This study uses machine learning approaches to model groundwater level (GWL) dynamics in six observation wells in the Ruataniwha Basin, New Zealand. These models are enhanced with seasonal decomposition techniques. This study uses both static properties and dynamic variables to capture hydrogeological heterogeneity. Random Forest (RF) and Support Vector Machine (SVM), with seasonal decomposition preprocessing, were developed for GWL modelling. The models were trained on 80% of the dataset and tested using the remaining 20% of the data. Model accuracy was assessed using five statistical metrics: mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination (R2), mean absolute percent error (MAPE), and percent bias (PBIAS). Model uncertainty was analyzed using Bayesian Model Averaging combined with the p-factor and d-factor at the 95% confidence level. The results demonstrate that both models delivered strong predictive performance across training, testing, and full period evaluations. However, the RF model demonstrated a marginally superior predictive accuracy by achieving lower errors (MAE: 0.013–0.174; RMSE: 0.04–0.283), better bias control (PBIAS ≈ 0%), and slightly tighter error bounds in most wells. Uncertainty quantification revealed that models provided a minimum p-factor of 0.878, capturing more than 87% of the observed GWL data within the uncertainty bounds. Comparing the results of both models, the RF model has higher p-factor values ranging from 0.878 to 0.976 with precise interval widths (d-factor: 0.436–0.769), indicating its reliability for adaptive groundwater management.
Kanito et al. (Wed,) studied this question.