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Many urban coastal communities are experiencing more profound flood impacts due to accelerated sea level rise that sometimes exceed their capacity to protect the built environment. In such cases, relocation may serve as a more effective hazard mitigation and adaptation strategy. However, it is unclear how urban residents living in flood-prone locations perceive the possibility of relocation and under what circumstances they would consider moving. Understanding the factors affecting an individual’s willingness to relocate because of coastal flooding is vital for developing accessible and equitable relocation policies. The main objective of this study is to identify the key considerations that would prompt urban coastal residents to consider permanent relocation because of coastal flooding. We leverage survey data collected from urban areas along the East Coast, assessing attitudes toward relocation, and design an artificial neural network (ANN) and a random forest (RF) model to find patterns in the survey data and indicate which considerations impact the decision to consider relocation. We trained the models to predict whether respondents would relocate because of socioeconomic factors, past exposure and experiences with flooding, and their flood-related concerns. Analyses performed on the models highlight the importance of flood-related concerns that accurately predict relocation behavior. Some common factors among the model analyses are concerns with increasing crime, the possibility of experiencing one more flood per year in the future, and more frequent business closures resulting from flooding.

Storm surge impact depends on coastal geographical and bathymetric features as well as various tropical cyclone characteristics including the size, intensity, and impact angle of the storm. Although the factors contributing to storm surge are well studied, uncertainties remain regarding the level of sensitivity to these parameters. This work seeks to contribute to the current knowledge of storm surge by studying the sensitivity to tropical cyclone landfall angle. We perform an ensemble of synthetic tropical cyclones using a newly developed modeling capability derived from the Weather Research and Forecasting (WRF) model, the Hybrid WRF Cyclone Model. Wind and atmospheric pressure field outputs from 200 synthetic cyclones are used as atmospheric forcing for the Advance Circulation (ADCIRC) model. We study the sensitivity of storm surge offshore extent and inundation to tropical cyclone impact angle. The extent of the impact area around the landfall location is sensitive to the cyclone landfall angle. Cyclones with tracks perpendicular to the coast are shown to produce the highest water levels and broadest inland and offshore extent. Results also indicate a heterogeneity in the sensitivity to landfall angle along the coast, highlighting the importance of both cyclone impact angle and location