Manuel Fiallos-Salguero, Khu Soon-Thiam, Guan Jingyu, Wang Mingna
School of Environmental Science & Engineering, Tianjin University, Tianjin, 300350, China.
School of Civil Engineering, Tianjin University, Tianjin, 300072, China.
Environ Sci Ecotechnol. 2025 Apr 24;25:100562. doi: 10.1016/j.ese.2025.100562. eCollection 2025 May.
Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management. However, traditional rainfall measurement methods face limitations regarding spatial coverage, temporal resolution, and data accessibility, particularly in urban settings. Here, we show a novel rainfall estimation framework that leverages surveillance cameras to enhance estimation accuracy and spatiotemporal data coverage. Our hybrid approach consists of two complementary modules: the first employs an image-quality signature technique to detect rain streaks from video frames and selects optimal regions of interest (ROIs). The second module integrates depthwise separable convolution (DSC) layers with gated recurrent units (GRU) in a regression model to accurately estimate rainfall intensity using these ROIs. We evaluate the framework using video data from two locations with distinct rainfall patterns and environmental conditions. The DSC-GRU model achieves high predictive performance, with coefficient of determination (R) values ranging from 0.89 to 0.93 when validated against rain gauge measurements. Remarkably, the model maintains strong performance during daytime and nighttime conditions, outperforming existing video-based rainfall estimation methods and demonstrating robust adaptability across variable environmental scenarios. The model's lightweight architecture facilitates efficient training and deployment, enabling practical real-time urban rainfall monitoring. This work represents a substantial advancement in rainfall estimation technology, significantly reducing estimation errors and expanding measurement coverage, and provides a practical, low-cost solution for urban hydrological monitoring.
高质量和高时空分辨率的降雨测量对于城市水文模型和有效的雨水管理至关重要。然而，传统的降雨测量方法在空间覆盖范围、时间分辨率和数据可获取性方面存在局限性，尤其是在城市环境中。在此，我们展示了一种新颖的降雨估计框架，该框架利用监控摄像头来提高估计精度和时空数据覆盖范围。我们的混合方法由两个互补模块组成：第一个模块采用图像质量特征技术从视频帧中检测雨线并选择最佳感兴趣区域（ROI）。第二个模块在回归模型中将深度可分离卷积（DSC）层与门控循环单元（GRU）集成，以使用这些ROI准确估计降雨强度。我们使用来自两个具有不同降雨模式和环境条件的地点的视频数据对该框架进行评估。DSC-GRU模型具有较高的预测性能，在与雨量计测量值进行验证时，决定系数（R）值范围为0.89至0.93。值得注意的是，该模型在白天和夜间条件下均保持强大性能，优于现有的基于视频的降雨估计方法，并在各种可变环境场景中表现出强大的适应性。该模型的轻量级架构便于高效训练和部署，实现实用的实时城市降雨监测。这项工作代表了降雨估计技术的重大进步，显著减少了估计误差并扩大了测量覆盖范围，并为城市水文监测提供了一种实用、低成本的解决方案。
