What question did this study set out to answer?

The aim is to improve groundwater table depth predictions using machine learning in data-limited regions.

March 21, 2026Open Access

A machine learning framework integrating data augmentation and physical principles for groundwater table depth prediction (Shiyang River Basin, China)

Key Points

The aim is to improve groundwater table depth predictions using machine learning in data-limited regions.
Integrated a Kalman filter for data alignment across heterogeneous datasets.
Utilized an entropy-based algorithm to identify key influencing factors.
Employed a Time-series Generative Adversarial Network (TimeGAN) for data augmentation.
Incorporated spatial characteristics to account for lateral groundwater flow.
Used SHAP to interpret feature importance related to groundwater dynamics.
Significantly improved prediction accuracy for groundwater table depth compared to traditional models.
TimeGAN enhanced the volume and diversity of training data, bolstering model performance.
The framework highlighted the critical role of lateral groundwater flow in dynamic prediction considerations.

Abstract

The Shiyang River Basin, an arid inland agricultural watershed in northwestern China, is characterized by intensive irrigation and long-term groundwater extraction that have caused widespread groundwater depletion and increasing pressure on regional water resources. Accurate prediction of groundwater table depth remains challenging in data-limited basins due to complex groundwater dynamics and sparse observations. To address these challenges, this study develops an integrated machine learning framework for monthly groundwater table depth prediction over a 20-year period. The framework combines a Kalman filter for harmonizing heterogeneous datasets, an entropy-based algorithm for identifying dominant influencing factors, and a Time-series Generative Adversarial Network for data augmentation. Spatial characteristics of groundwater table depth are further incorporated to represent the influence of lateral groundwater flow. SHAP-based interpretation reveals that lateral groundwater flow exerts a substantial influence on groundwater dynamics across the basin, highlighting the importance of representing spatial groundwater interactions in data-driven prediction. The proposed framework significantly improves groundwater table depth prediction accuracy compared with conventional machine learning models. This approach provides a robust and interpretable tool for groundwater table depth prediction in arid, data-scarce basins and supports more effective groundwater management in intensively irrigated regions. • We integrated data alignment, data augmentation, and groundwater spatial influence to improve GTD prediction in data-scarce regions. • TimeGAN generates synthetic sequences to augment training data volume and diversity, significantly improving model performance. • Introducing groundwater spatial distribution to characterize lateral flow as physical constraints effectively enhanced the prediction. • SHAP quantified feature contributions and confirmed the key role of lateral flow in prediction for intensive groundwater mining zones.

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