Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management
[4];
- DHI Vietnam (Vietnam)
- Southern Institute of Water Resources Research (Vietnam)
- Southern Regional Hydrometeorological Center (Vietnam)
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- FPT University (Vietnam)
- Queen's Univ., Belfast, Northern Ireland (United Kingdom)
- Van Lang University (Vietnam)
The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2477500
- Journal Information:
- Environmental Modelling and Software, Journal Name: Environmental Modelling and Software Vol. 183; ISSN 1364-8152
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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