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Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework
Fiallos-Salguero Manuel1, Soon-Thiam Khu1, Jingyu Guan2
1School of Environmental Science & Engineering, Tianjin University, Tianjin, 300350, China.
Environmental Science and Ecotechnology
|May 20, 2025
Summary
This study introduces a new framework using surveillance cameras for accurate rainfall estimation, improving urban hydrological modeling and stormwater management. The novel approach enhances data coverage and accuracy, offering a practical solution for real-time monitoring.
Area of Science:
- Hydrology and Remote Sensing
- Computer Vision for Environmental Monitoring
Background:
- High-quality rainfall data is crucial for urban hydrology and stormwater management.
- Traditional methods have limitations in spatial coverage, temporal resolution, and accessibility in urban areas.
Purpose of the Study:
- To develop a novel rainfall estimation framework using surveillance cameras.
- To enhance accuracy and spatiotemporal data coverage for rainfall measurement.
Main Methods:
- A hybrid approach combining image-quality signature for rain streak detection and optimal region selection.
- Integration of depthwise separable convolution (DSC) and gated recurrent units (GRU) for rainfall intensity estimation.
Main Results:
- The DSC-GRU model achieved high predictive performance (R² values of 0.89-0.93) against rain gauge data.
- The framework demonstrated robust performance in both daytime and nighttime conditions across diverse environments.
- Outperformed existing video-based rainfall estimation methods.
Conclusions:
- The novel framework provides a practical, low-cost solution for real-time urban rainfall monitoring.
- Significantly reduces estimation errors and expands measurement coverage for hydrological applications.
- Represents a substantial advancement in rainfall estimation technology.
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