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

TL;DR

This paper presents a machine learning pipeline that improves groundwater level prediction accuracy, enabling early warning and resource management through automated modeling and geospatial data integration.

Contribution

It introduces an automated ensemble machine learning framework tailored for large-scale groundwater monitoring, addressing data sparsity and delayed outputs.

Findings

Achieved high validation weighted F1 score of 0.927

Demonstrated practical utility for early warning systems

Provided an open-source scalable framework

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.