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A critical review of real-time modelling of flood forecasting in urban drainage systems

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  • NSTL
  • Elsevier
There has been a strong tendency in recent decades to develop real-time urban flood prediction models for early warning to the public due to a large number of worldwide urban flood occurrences and their disastrous consequences. While a significant breakthrough has been made so far, there are still some potential knowledge gaps that need further investigation. This paper presents a comprehensive review of the current state-of-the-art and future trends of real-time modelling of flood forecasting in urban drainage systems. Findings showed that the combination of various real-time sources of rainfall measurement and the inclusion of other real-time data such as soil moisture, wind flow patterns, evaporation, fluvial flow and infiltration should be more investigated in real-time flood forecasting models. Additionally, artificial intelligence is also present in most of the new RTFF models in UDS and consequently further developments of this technique are expected to appear in future works.

Artificial intelligence-based modelsData -driven modelsReal -time flood forecastingUrban drainage systemsUrban floodARTIFICIAL NEURAL-NETWORKSRAINFALL DATAOPTIMIZATION TECHNIQUESPRECIPITATION PRODUCTSDATA-DRIVENRADAR DATAGAUGERESOLUTIONURBANIZATIONCOMBINATION

Piadeh, Farzad、Behzadian, Kourosh、Alani, Amir M.

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Univ West London

2022

10.1016/j.jhydrol.2022.127476

Journal of Hydrology

Journal of Hydrology

EISCI
ISSN：0022-1694
年,卷(期)：2022.607
  • 20
  • 120