TabGRU: An Enhanced Design for Urban Rainfall Intensity Estimation Using Commercial Microwave Links

TL;DR

This paper introduces TabGRU, a hybrid deep learning model combining Transformer and BiGRU, to improve urban rainfall estimation from Commercial Microwave Links, outperforming traditional physics-based models and existing deep learning approaches.

Contribution

The paper presents a novel hybrid deep learning architecture, TabGRU, that enhances rainfall estimation accuracy from CML data by capturing long-term and local features with attention mechanisms.

Findings

TabGRU outperforms baseline deep learning models with R2 of 0.91 and 0.96.

It effectively reduces overestimation during peak rainfall events.

The model demonstrates robustness on real-world urban rainfall data.

Abstract

In the face of accelerating global urbanization and the increasing frequency of extreme weather events, highresolution urban rainfall monitoring is crucial for building resilient smart cities. Commercial Microwave Links (CMLs) are an emerging data source with great potential for this task.While traditional rainfall retrieval from CMLs relies on physicsbased models, these often struggle with real-world complexities like signal noise and nonlinear attenuation. To address these limitations, this paper proposes a novel hybrid deep learning architecture based on the Transformer and a Bidirectional Gated Recurrent Unit (BiGRU), which we name TabGRU. This design synergistically captures both long-term dependencies and local sequential features in the CML signal data. The model is further enhanced by a learnable positional embedding and an attention pooling mechanism to improve its dynamic feature extraction and generalization capabilities. The model was validated on a public benchmark dataset from Gothenburg, Sweden (June-September 2015). The evaluation used 12 sub-links from two rain gauges (Torp and Barl) over a test period (August 22-31) covering approximately 10 distinct rainfall events. The proposed TabGRU model demonstrated consistent advantages, outperforming deep learning baselines and achieving high coefficients of determination (R2) at both the Torp site (0.91) and the Barl site (0.96). Furthermore, compared to the physics-based approach, TabGRU maintained higher accuracy and was particularly effective in mitigating the significant overestimation problem observed in the PL model during peak rainfall events. This evaluation confirms that the TabGRU model can effectively overcome the limitations of traditional methods, providing a robust and accurate solution for CML-based urban rainfall monitoring under the tested conditions.