Assessing the impact of geo-targeted warning messages on residents’ evacuation decisions before a hurricane using agent-based modeling

Gao, Songxin; Wang, Yan

doi:10.1007/s11069-021-04576-1

Cited by 10 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conceptual model defined in Figure 1 was implemented into an ABM in the program NetLogo (Wilensky, 1999) to enable simulations of hurricane warning diffusion scenarios for areas of interest. ABMs have been used in prior simulations of hurricane response (Yin et al, 2014(Yin et al, , 2016Gao and Wang, 2021;Harris et al, 2022). Use of ABMs in understanding hurricane warning diffusion in a population might support improved practices in risk communication and hazard planning.…”

Section: Development In Netlogomentioning

confidence: 99%

Agent-Based Simulation of Hurricane Risk Information Dissemination

Yeates,

Chen,

Lindell

et al. 2023

Int. Conf. Coastal. Eng.

View full text Add to dashboard Cite

The effectiveness of official warning dissemination and interpretation determines the adoption of protective actions in response to hurricane threat. This paper describes the development of a conceptual warning diffusion model that articulates the whole process of the hurricane warning diffusion and individual decision-making, drawing on emergency management and social science bodies of literature. This conceptual model is then partially encoded into a NetLogo agent-based simulation model to demonstrate the effects of various warning diffusion components on evacuation decisions for areas of interest. The translation of the conceptual model into agent-based simulations can inform the coastal engineering and science communities of the importance of communicating risk information to the public, and also helps to identify future research needed to illuminate hurricane risk communication pathways.

show abstract

Section: Development In Netlogomentioning

confidence: 99%

Agent-Based Simulation of Hurricane Risk Information Dissemination

Yeates,

Chen,

Lindell

et al. 2023

Int. Conf. Coastal. Eng.

View full text Add to dashboard Cite

show abstract

“…Agent-based modeling is often performed for evacuation modeling and planning, such as modeling evacuation vehicles (Chen and Zhan, 2014) and assessing warning messages with geographical targets (Gao and Wang, 2021). However, computational burden is a major limitation.…”

Section: Human Mobility Data and Analysis Applications In Disaster Ri...mentioning

confidence: 99%

Human mobility data and analysis for urban resilience: A systematic review

Haraguchi

Nishino

Kodaka

et al. 2022

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.

show abstract

“…First, the hurricane risk levels of large spatial regions cannot fully represent the risk levels of households of different housing types (e.g., single‐family houses, multifamily houses, or mobile houses), which were the basis of the risk mitigation behaviors of local households (Chatterjee & Mozumder, 2014). Second, because the detailed features of individual buildings and their surrounding environments were rarely considered (Gao & Wang, 2021), the hurricane risk assessment at large spatial scales may only describe the general hurricane impact instead of the damages to specific individual buildings. Additionally, the fragility functions mainly captured the building components’ performance in ideal experiment scenarios, while the real‐world building performance in hurricane events cannot be fully represented by these fragility functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable deep learning powered building risk assessment model for proactive hurricane response

Gao

Wang

2022

Risk Analysis

View full text Add to dashboard Cite

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building‐level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine‐grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model‐agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep‐learning models in integrating multi‐sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI‐powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long‐term climate adaptation.

show abstract

Assessing the impact of geo-targeted warning messages on residents’ evacuation decisions before a hurricane using agent-based modeling

Cited by 10 publications

References 40 publications

Agent-Based Simulation of Hurricane Risk Information Dissemination

Agent-Based Simulation of Hurricane Risk Information Dissemination

Human mobility data and analysis for urban resilience: A systematic review

Explainable deep learning powered building risk assessment model for proactive hurricane response

Contact Info

Product

Resources

About