Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Zheng, Feifei (Zhejiang University)
Yin, Hang (Zhejiang University)
Ma, Yiyi (Zhejiang University)
Duan, Huan Feng (The Hong Kong Polytechnic University)
Gupta, Hoshin (University of Arizona)
Savic, Dragan (KWR Water Research Institute; University of Exeter; Universiti Kebangsaan Malaysia)
Kapelan, Z. (TU Delft Sanitary Engineering; University of Exeter)

2023

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground-based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real-time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost.

convolutional neural networks (CNNs)
deep learning
extraction of raindrop information
image decomposition
rainfall intensity estimation
surveillance camera imagery
urban flooding

http://resolver.tudelft.nl/uuid:f38f10b0-31d2-4b66-b81a-c5086cea89ee

https://doi.org/10.1029/2023WR034831

2024-02-15

0043-1397

Water Resources Research, 59 (8)

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Feifei Zheng, Hang Yin, Yiyi Ma, Huan Feng Duan, Hoshin Gupta, Dragan Savic, Z. Kapelan