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Research on an urban flood early warning model based on multi-source data collaborative perception.

Dafeng Gong1, Linggui Meng2, Aichun Lin2

  • 1School of Intelligent Manufacturing, Wenzhou Polytechnic, Chashan Higher Education Park, Wenzhou City, Zhejiang Province, China.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
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Summary
This summary is machine-generated.

This study introduces an advanced urban waterlogging early warning model. It improves accuracy and timeliness in predicting and managing urban flood risks, aiding disaster relief efforts.

Keywords:
collaborative perceptionearly warningmulti-source dataurban waterlogging

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Area of Science:

  • Environmental Engineering
  • Urban Hydrology
  • Geospatial Science

Background:

  • Urban waterlogging poses significant threats to city operations and resident safety.
  • Accurate and efficient early warning systems are crucial for mitigating waterlogging impacts.

Purpose of the Study:

  • To develop and validate an improved urban waterlogging early warning model.
  • To integrate multi-source data for enhanced flood risk assessment.

Main Methods:

  • Multi-source data fusion (meteorology, hydrology, geospatial, drainage).
  • Real-time data processing via distributed computing.
  • Application of hydrological models (Horton infiltration, isochron, Saint-Venant equations, Hazen-Williams formula).
  • Dynamic adjustment of waterlogging risk levels.

Main Results:

  • Precise simulation of surface runoff and urban drainage capacity.
  • Demonstrated improvements in accuracy, reliability, timeliness, and spatial precision of early warnings.
  • Comparative analysis showing superiority over existing models (AquaTalk, MIKE FLOOD, CAE S.p.A., FIEDLER).

Conclusions:

  • The proposed model offers a significant advancement in urban waterlogging early warning.
  • Provides a valuable reference for urban waterlogging prevention and disaster management strategies.