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

Feifei Zheng, Hang Yin, Yiyi Ma, Huan Feng Duan, Hoshin Gupta, Dragan Savic, Zoran Kapelan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article numbere2023WR034831
JournalWater Resources Research
Issue number8
StatePublished - Aug 2023


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

ASJC Scopus subject areas

  • Water Science and Technology


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