Groundwater level forecasting using data-driven models and vadose zone: A comparative analysis of ARIMA, SARIMAX, Prophet, and NeuralProphet

Forecasting water availability is becoming increasingly vital due to rising human demands and changing climatic pressures have caused declines in groundwater levels across many regions. While numerous studies employ data-driven approaches to predict groundwater fluctuations using meteorological data and groundwater level observations, few incorporate measurements from the vadose zone into predictive models. This study proposes a novel method leveraging an advanced hydrogeological monitoring system with high spatio-temporal resolution to forecast groundwater levels in a shallow alluvial aquifer used for drinking purposes. The monitoring system comprises a thermo-pluviometric station and three probes that measure soil water content, electrical conductivity, and temperature at depths of 0.6, 0.9, and 1.7 meters, in addition to a piezometer with a permanent water-level sensor. Data was collected at 15 min intervals over two hydrological years and integrated as exogenous inputs to enhance model predictive performance. Statistical, machine learning and deep learning architectures were tested through ARIMA, SARIMAX, Prophet and NeuralProphet providing a comprehensive evaluation of different approaches. For a robust evaluation, a rolling K-fold cross-validation strategy was implemented and coupled with a grid search to fine-tune all the models. Evaluation metrics and correlation coefficients are employed to assess the predictive capabilities of each model. Our findings indicate that prediction accuracy improves across all models with increasing depth in the vadose zone, with machine learning and deep learning models showing the most significant improvements. Specifically, at 1.7 m depth, Prophet achieves a MAPE of 4.5%, and NeuralProphet achieves a MAPE of 4.1% compared to statistical models. This study has successfully highlighted the enhancement of AI-based models for estimating levels of groundwater incorporating subsurface information from the vadose zone at different depths and phreatic zones, alongside climatic variables.