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Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
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Flood forecasting within urban drainage systems using NARX neural network.

Yves Abou Rjeily1, Oras Abbas2, Marwan Sadek1

  • 1Lille University of Science and Technology, Laboratoire de Génie Civil et Géo-Environnement, Villeneuve d'Ascq, France E-mail: yves.abourjeily@hotmail.com; Lebanese University, Modeling Center, Beirut, Lebanon.

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Summary

Urban flooding is a growing problem due to increased runoff. This study developed a flooding forecast system (FFS) using a NARX neural network to predict urban drainage system issues and mitigate flood risks.

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Area of Science:

  • Environmental Engineering
  • Hydrology
  • Artificial Intelligence

Background:

  • Urbanization and climate change exacerbate urban drainage system (UDS) runoff volumes, leading to capacity issues and flooding.
  • Aging infrastructure and structural failures further limit UDS capacity, increasing the occurrence of urban floods.
  • Proactive management and flood mitigation are crucial for UDS operators facing increased flooding events.

Purpose of the Study:

  • To develop an advanced flooding forecast system (FFS) for early detection of potential flooding events in urban areas.
  • To provide UDS managers with timely alerts for proactive flood mitigation strategies.
  • To enable rapid estimation of water depth variations in critical manholes for reliable flood risk assessment.

Main Methods:

  • Development of a Flooding Forecast System (FFS) utilizing a Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network.
  • The NARX model was selected for its capability to correlate water depth variations in manholes with rainfall intensities.
  • Experimental validation of the FFS was conducted at the University of Lille campus.

Main Results:

  • The developed FFS successfully estimates water depth variations in critical manholes based on forecasted rainfall.
  • The system provides advance warnings to UDS operators, enabling proactive flood management.
  • The NARX neural network proved effective in modeling the complex dynamics of urban drainage systems.

Conclusions:

  • The proposed FFS offers a reliable tool for predicting urban flooding events, enhancing UDS management.
  • Early flood detection through the FFS supports efficient flood mitigation and reduces urban flood impacts.
  • The integration of NARX neural networks presents a promising approach for real-time urban drainage monitoring and flood forecasting.