ML-Based Virtual Sensing for Groundwater Monitoring in the Netherlands
conference paper
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. © 2025 by SCITEPRESS – Science and Technology Publications, Lda
Topics
TNO Identifier
1013302
ISBN
978-989-758-737-5
Publisher
Science and Technology Publications, Lda
Source title
17th International Conference on Agents and Artificial Intelligence, ICAART 2025, 23 February through 25 February 2025, Porto
Editor(s)
Rocha A.P.
Steels L.
Herik H.J van den
Steels L.
Herik H.J van den
Pages
175-184
Files
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