Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation
This work addresses groundwater management for ecosystems, agriculture, and drinking water supplies, offering a scalable framework for early warning and resource allocation, though it is incremental as it applies existing automated methods to a new domain.
The authors tackled the challenge of groundwater monitoring by developing a machine learning pipeline that predicts groundwater level categories, achieving weighted F1 scores of 0.927 on validation and 0.67 on test data from a large-scale French dataset.
Groundwater supports ecosystems, agriculture, and drinking water supplies worldwide, yet effective monitoring remains challenging due to sparse data, computational constraints, and delayed outputs from traditional approaches. We develop a machine learning pipeline that predicts groundwater level categories using climate data, hydro-meteorological records, and physiographic attributes processed through AutoGluon's automated ensemble framework. Our approach integrates geospatial preprocessing, domain-driven feature engineering, and automated model selection to overcome conventional monitoring limitations. Applied to a large-scale French dataset (n $>$ 3,440,000 observations from 1,500+ wells), the model achieves weighted F\_1 scores of 0.927 on validation data and 0.67 on temporally distinct test data. Scenario-based evaluations demonstrate practical utility for early warning systems and water allocation decisions under changing climate conditions. The open-source implementation provides a scalable framework for integrating machine learning into national groundwater monitoring networks, enabling more responsive and data-driven water management strategies.