Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation

• URL: http://arxiv.org/abs/2506.22461v1
• Date: Wed, 18 Jun 2025 00:41:04 GMT
• Title: Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation
• Authors: Chuan Li, Ruoxuan Yang,
• Abstract summary: We develop a machine learning pipeline that predicts groundwater level categories using climate data, hydro-meteorological records, and physiographic attributes.<n>Our approach integrates geospatial preprocessing, domain-driven feature engineering, and automated model selection to overcome monitoring limitations.
• Score: 1.372066170415575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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