Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

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. 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. • EUFWs forecast urban flooding in Ho Chi Minh City, Vietnam. • EUFWs have been efficiently deployed and integrated through a real-time measuring network. • The real-time warning system of EUFWS is integrated with a user-friendly web and app interface. • EUFWS has been successfully integrated and implemented in Thu Duc City operation & controlling center.

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Publication Details

Journal
Environmental Modelling & Software
Published
2024-10-10
DOI
https://doi.org/10.1016/j.envsoft.2024.106246
Citations
8
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
2.26
Controls
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article

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

Ahad Hasan Tanim, Van Hieu Bui, Duong Tran Anh, Son T. et al.
8 citations
Environmental Modelling & Software
Flood Risk Assessment and Management
2.26
article

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

Ahad Hasan Tanim, Van Hieu Bui, Duong Tran Anh, Son T., Phong Nguyen Thanh, Quang Tri Le, Ba Hoang Tran, Thanh Quang Dang
article en
8 citations

Abstract

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. 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. • EUFWs forecast urban flooding in Ho Chi Minh City, Vietnam. • EUFWs have been efficiently deployed and integrated through a real-time measuring network. • The real-time warning system of EUFWS is integrated with a user-friendly web and app interface. • EUFWS has been successfully integrated and implemented in Thu Duc City operation & controlling center.

Environmental Modelling & SoftwareVol. 183
FPT University (VN), Queen's University Belfast (GB), Oak Ridge National Laboratory (US), Thuyloi University (VN), Van Lang University (VN)
Climate action
Openalex Percentile: Top 12%
Flood Risk Assessment and Management
2.26
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