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

• URL: http://arxiv.org/abs/2512.02465v1
• Date: Tue, 02 Dec 2025 06:50:50 GMT
• Title: TabGRU: An Enhanced Design for Urban Rainfall Intensity Estimation Using Commercial Microwave Links
• Authors: Xingwang Li, Mengyun Chen, Jiamou Liu, Sijie Wang, Shuanggen Jin, Jafet C. M. Andersson, Jonas Olsson, Remco, van de Beek, Hai Victor Habi, Congzheng Han,
• Abstract summary: This paper proposes a novel hybrid deep learning architecture based on the Transformer and a Bidirectional Gated Recurrent Unit (BiGRU)<n>The model was validated on a public benchmark dataset from Gothenburg, Sweden.
• Score: 21.629452553604512
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Diffusion-Based Framework for High-Resolution Precipitation Forecasting over CONUS [0.410492188035848]
  This study introduces a diffusion-based deep learning (DL) framework that systematically compares three residual prediction strategies differing only in their input sources.<n>Forecasts are produced at 1-km spatial resolution, beginning with direct 1-hour predictions and extending to 12 hours using autoregressive rollouts.
  arXiv  Detail & Related papers  (2025-12-09T19:23:27Z)
• CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting [6.540270371082014]
  This study develops a deep learning-based ensemble framework for multi-step precipitation prediction.<n>The architecture employs a patch-based Swin Transformer backbone with periodic convolutions to handle longitudinal continuity.<n>Training minimizes a hybrid loss combining the Continuous Ranked Probability Score (CRPS) and weighted log1p mean squared error (log1pMSE)
  arXiv  Detail & Related papers  (2025-10-23T17:43:38Z)
• SEMPO: Lightweight Foundation Models for Time Series Forecasting [45.456949943052116]
  SEMPO is a lightweight foundation model that requires pretraining on relatively small-scale data, yet exhibits strong general time series forecasting.<n> SEMPO comprises two key modules: 1) energy-aware SpEctral decomposition module, that substantially improves the utilization of pre-training data.<n>Experiments on two large-scale benchmarks covering 16 datasets demonstrate the superior performance of SEMPO in both zero-shot and few-shot forecasting scenarios.
  arXiv  Detail & Related papers  (2025-10-22T15:58:44Z)
• Km-scale dynamical downscaling through conformalized latent diffusion models [45.94979929172337]
  Dynamical downscaling is crucial for deriving high-resolution meteorological fields from coarse-scale simulations.<n>Generative Diffusion models (DMs) have recently emerged as powerful data-driven tools for this task.<n>However, DMs lack finite-sample guarantees against overconfident predictions, resulting in miscalibrated grid-point-level uncertainty estimates.<n>We tackle this issue by augmenting the downscaling pipeline with a conformal prediction framework.
  arXiv  Detail & Related papers  (2025-10-15T08:41:36Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• TopoFormer: Integrating Transformers and ConvLSTMs for Coastal Topography Prediction [0.0]
  textitTopoFormer integrates transformer-based encoders with convolutional long short-term memory layers.<n>The architecture captures both long-range dependencies and localized temporal patterns inherent in beach profiles data.
  arXiv  Detail & Related papers  (2025-01-11T09:46:02Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
  Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
  arXiv  Detail & Related papers  (2023-07-29T22:49:50Z)
• A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia [0.0]
  Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides. High-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, but their computational complexity renders them unsuitable for real-time applications. This study compares the performance of a surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
  arXiv  Detail & Related papers  (2023-07-26T13:24:01Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG [1.9860735109145415]
  We use Recurrent and Convolutional deep neural network structures to address the challenge of precipitation nowcasting. A total of five models are trained using Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data over the Eastern Contiguous United States (CONUS) The models were designed to provide forecasts with a lead time of up to 1.5 hours and, by using a feedback loop approach, the ability of the models to extend the forecast time to 4.5 hours was also investigated.
  arXiv  Detail & Related papers  (2021-08-16T02:55:32Z)
• Stream-Flow Forecasting of Small Rivers Based on LSTM [3.921808417990452]
  This paper tries to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model. We collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)
  arXiv  Detail & Related papers  (2020-01-16T07:14:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.