ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands

Laure Grisez, Shreshtha Sharma, Paolo Pileggi

2025

Abstract

The increasing need for effective groundwater monitoring presents a valuable opportunity for Machine Learning (ML)-based virtual sensing, especially in regions with challenging sensor networks. This paper studies the practical application of two core ML models, Gaussian Process Regression (GPR) and Position Embedding Graph Convolutional Network (PEGCN), for predicting groundwater levels in The Netherlands. Additionally, other models, such as Graph Convolutional Networks and Graph Attention Networks, are mentioned for completeness, offering a broader understanding of ML methods in this domain. Through two experiments, sensor data reconstruction and virtual sensor prediction, we consider model performance, ease of implementation, and computational requirements. Practical lessons are drawn, emphasising that while advanced models like PEGCN excel in accuracy for complex environments, simpler models like GPR are better suited for non-experts due to their ease of use and minimal computational overhead. These insights highlight the trade-offs between accuracy and usability, with important considerations for real-world deployment by practitioners less familiar with ML.

Paper Citation

in Harvard Style

Grisez L., Sharma S. and Pileggi P. (2025). ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 175-184. DOI: 10.5220/0013101100003890

in Bibtex Style

@conference{icaart25,
author={Laure Grisez and Shreshtha Sharma and Paolo Pileggi},
title={ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={175-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013101100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands
SN - 978-989-758-737-5
AU - Grisez L.
AU - Sharma S.
AU - Pileggi P.
PY - 2025
SP - 175
EP - 184
DO - 10.5220/0013101100003890
PB - SciTePress