Speaker
Description
Accurately measuring rainfall is essential for weather forecasting, flood prediction, and water resource management. Traditional methods rely on rain gauges for direct measurements, radar systems for broader coverage and satellites. However, these methods face challenges due to sparse sensor distribution and data coverage.
A promising alternative is using wireless commercial microwave links (CMLs)—the infrastructure behind cellular networks. CMLs experience signal attenuation when it rains, allowing them to serve as cost-effective, high-resolution rainfall virtual sensors. However, current training machine learning models require paired CML-rain gauge data, which limits their applicability due to missing or misaligned measurements.
To overcome this limitation, we propose a CycleGAN-based framework that enables rainfall estimation without requiring paired datasets. Instead of relying on direct matches between CMLs and rain gauges, our method learns the relationship between the two through an unpaired training strategy.
We introduce two mapping functions:
• G:A→R (Converts attenuation to rain rate).
• F:R→A (Converts rain rate to attenuation).
By enforcing cycle consistency, the model ensures that translating between the two domains preserves data structure, even in the absence of direct pairing between a CML and a gauge.
Our method offers several key advantages:
• Works with missing or sparse data.
• Adapts to different regions without direct alignment.
• Enhances rain estimation accuracy with a built-in detector.
We evaluated our approach on real-world CML datasets and rain gauge data from Israel and the Netherlands, demonstrating high accuracy in estimating accumulated rainfall, especially in heavy rain events.
This framework expands the capabilities of deep learning for rainfall estimation by enabling models to learn from unpaired datasets. It provides a scalable and flexible solution that overcomes the limitations of traditional supervised approaches.
| Are you an Early Career Scientist ? | Yes |
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