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

2025-12-02

AI Summary

To address the need for high-resolution urban rainfall monitoring, this study proposes TabGRU—a novel hybrid deep learning architecture integrating Transformer and bidirectional gated recurrent units (BiGRU), enhanced with learnable positional encoding and attention-based pooling to jointly capture long-range dependencies and local temporal dynamics in commercial microwave link (CML) signals. Unlike conventional physical models, which suffer from noise sensitivity, nonlinear attenuation effects, and severe overestimation during heavy rainfall, TabGRU enables end-to-end rainfall intensity retrieval. Evaluated at two CML sites in Gothenburg, Sweden—Torp and Barl—the model achieves R² scores of 0.91 and 0.96, respectively, significantly outperforming both state-of-the-art physical models and deep learning baselines. The proposed approach enhances estimation accuracy and robustness under extreme weather conditions.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Estimates urban rainfall intensity using commercial microwave links
• Overcomes signal noise and nonlinear attenuation in traditional methods
• Enhances accuracy and reduces overestimation during peak rainfall events

Innovation

• Hybrid Transformer-BiGRU architecture for rainfall estimation
• Learnable positional embedding and attention pooling mechanisms
• Outperforms physics-based models in accuracy and overestimation mitigation