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

Created by
  • Haebom

Author

Chuan Li, Ruoxuan Yang

Outline

To overcome the challenges of traditional groundwater monitoring (lack of data, computational constraints, delayed results), we developed a machine learning pipeline that processes climate data, hydrometeorological records, and topographic features using AutoGluon’s automatic ensemble framework. Applied to a large dataset from France (>3,440,000 observations from >1,500 wells), we achieved a weighted F_1 score of 0.927 for validation data and 0.67 for temporally separated test data. Scenario-based assessments demonstrate its practical utility for early warning systems and water allocation decisions under climate change conditions. The open source implementation provides a scalable framework to integrate machine learning into national groundwater monitoring networks, enabling more rapid and data-driven water management strategies.

Takeaways, Limitations

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Takeaways:
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Presenting an efficient and scalable machine learning-based monitoring system for groundwater level prediction.
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Early warning system for groundwater management under climate change and support for water allocation decisions.
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Offers the possibility of expansion to other regions and countries through open source disclosure.
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Automated machine learning pipelines can be applied without expert knowledge.
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Limitations:
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The F_1 score (0.67) for the temporally separated test data is significantly lower than that for the validation data (0.927). Generalization performance needs to be improved.
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Lack of consideration of additional factors (e.g. human activity) that may affect the model's predictive accuracy.
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The dataset used was limited to France, so further research is needed to determine generalizability to other regions.
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