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.
@article{arxiv.2506.22461,
title = {Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation},
author = {Chuan Li and Ruoxuan Yang},
journal= {arXiv preprint arXiv:2506.22461},
year = {2025}
}