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

2025/06/18 by Li Chuan, Li, Chuan, Yang, Ruoxuan
Earth and Planetary Sciences · Environmental Science · #Artificial Intelligence (cs.AI) #Environmental Monitoring and Data Management #FOS: Computer and information sciences #FOS: Electrical engineering #Groundwater and Watershed Analysis #Hydrological Forecasting Using AI #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.22461

openalex publication_date 2025/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
• Towards Learning Universal, Regional, and Local Hydrological Behaviors\n via Machine-Learning Applied to Large-Sample Datasets
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
• GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

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