Assessing Disaster Impact in Real Time: Data-Driven System Integrating Humans, Hazards, and the Built Environment

Hao, Haiyan; Yan, Wei

doi:10.1061/(asce)cp.1943-5487.0000970

Cited by 8 publications

(1 citation statement)

References 46 publications

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“…With the development of forecasting technology and AI‐enabled forecast models, the input data for the wind and surge‐related features are also expected to be more accurate. Our developed DFNN models have the agility to intake outputs from future forecast models as well; our future work will keep tuning the model with outputs from new weather forecast models and other real‐time crowdsourcing data sources (Yao & Wang, 2020; Hao & Wang, 2021). Second, lacking data on the damage conditions of specific building components, our DFNN models only assess the overall level of each building's potential damages caused by hurricane winds and surges.…”

Section: Discussionmentioning

confidence: 99%

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

Gao

Wang

2022

Risk Analysis

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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.

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Section: Discussionmentioning

confidence: 99%