Instant Flood Risk Modelling (Inform) Tool for Co-Design of Flood Risk Management Strategies with Stakeholders in Can Tho City, Vietnam

Ngo, Hieu Quang; Radhakrishnan, Mohanasundar; Ranasinghe, Roshanka; Pathirana, Assela; Zevenbergen, Chris

doi:10.3390/w13213131

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…They focus on a 36-hour flood forecast at street-level (Loftis et al, 2019), a 60-hour flood risk forecast including soil saturation data (Alcantara et al, 2018), and a weekly flood forecast based on a hydrological model (Artinyan et al, 2016). Some of these systems are cloud-based and use GPU to speed up model run time compared to CPU for predicting effects on transportation infrastructure systems (Morsy et al, 2018), or presents a tool for modeling flood risk instantly (Ngo et al, 2021).…”

Section: Flood Information Systemsmentioning

confidence: 99%

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

Yeşilköy,

Baydaroğlu,

Singh

et al. 2024

Preprint

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Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facilitating proactive responses. Today's information systems offer a vast and intricate mesh of data, encompassing satellite imagery, meteorological metrics, and predictive modeling. Easily accessible to the general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids to decision-making processes. This review collates key literature on water-related disaster information systems, underscoring the transformative impact of emerging information and Internet technologies. These advancements promise enhanced flood and drought warning timeliness and greater preparedness through improved management, analysis, visualization, and data sharing. Moreover, these systems aid in hydrometeorological predictions, foster the development of web-based educational platforms, and support decision-making frameworks, digital twins, and metaverse applications in disaster contexts. They further bolster scientific research and development, enrich climate change vulnerability frameworks, and strengthen associated cyberinfrastructures. This article delves into prospective developments in the realm of natural disasters, pinpointing primary challenges and gaps in current water-related disaster information systems, and highlighting the potential intersections with future artificial intelligence solutions.

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Section: Flood Information Systemsmentioning

confidence: 99%

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

Yeşilköy,

Baydaroğlu,

Singh

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Vietnam also holds great emphasis on employing digital information to create data-driven environmental policies. With the growing availability of data and data analysis tools [9], Vietnam has developed its own national disaster database: the Damage and Needs Assessment system (DANA) to better assess the damages caused by flood disaster [10]. The Vietnamese government has also developed strategies to control and mitigate flood risk using a variety of strategies, including preventive measure such as building dikes, dams to divert river flow, and early warning communications system for evacuation measures.…”

Section: Introductionmentioning

confidence: 99%

Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach

Quang

Tallam

2022

Sustainability

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Flooding as a hazard has negatively impacted Vietnam’s agriculture, economy, and infrastructure with increasing intensity because of climate change. Flood hazards in Vietnam are difficult to combat, as Vietnam is densely populated with rivers and canals. While there are attempts to lessen the damage through hazard mitigation policies, such as early evacuation warnings, these attempts are made heavily reliant on short-term traditional statistical models and physical hydrology modeling, which provide suboptimal results. The current situation is caused by the fragmented approach from the Vietnamese government and exacerbates a need for more centralized and robust flood predictive systems. Local governments need to employ their own prediction models which often lack the capacity to draw key insights from limited flood occurrences. Given the robustness of machine learning, especially in low data settings, in this study, we attempt to introduce an artificial neural network model with the aim to create long-term forecast and compare it with other machine learning approaches. We trained the models using different variables evaluated under three characteristics: climatic, hydrological, and socio-economic. We found that our artificial neural network model performed substantially better both in performance metrics (91% accuracy) and relative to other models and can predict well flood hazards in the long term.

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On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Duong,

Meynadier,

Ranasinghe

et al. 2024

npj Nat. Hazards

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Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

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Instant Flood Risk Modelling (Inform) Tool for Co-Design of Flood Risk Management Strategies with Stakeholders in Can Tho City, Vietnam

Cited by 3 publications

References 38 publications

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

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