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Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. 

Florida's emergency relief operations were significantly affected by recent hurricanes such as Hermine and Irma that caused massive roadway and power system distributions. During these recent devastating hurricanes, the problems associated with providing accessibility and safety became even more challenging, especially for those vulnerable communities and disadvantaged segments of the society, such as aging populations were considered - that is, those who need and benefit from the emergency services the most. This complexity is magnified in states like Florida, considering the diverse physical, cognitive, economic and demographic variation of its population. As such, with a major focus on real-life data on roadway closures and power outages for the Hurricane Hermine, combined resilience (co-resilience) of emergency response facilities in the City of Tallahassee, the capital of Florida, was extensively studied based on the (a) temporal reconstruction of the reported power outages and roadway closures, and (b) development of co-resilience metrics to identify and visually map the most affected power system feeders and transportation network locations. Results show those regions with reduced emergency response facility accessibility, and those power lines and roadways under a disruption risk after Hermine hit Tallahassee. 

Abstract: Load forecasting plays a very crucial role in many aspects of electric power systems including the economic and social benefits. Previously, there have been many studies involving load forecasting using time series approach, including weather-load relationships. In one such approach to predict load, this paper investigates through different structures that aim to relate various daily parameters. These parameters include temperature, humidity and solar radiation that comprises the weather data. Along with natural phenomenon as weather, physical aspects such as traffic flow are also considered. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered. Electricity consumption data is collected from the City of Tallahassee utilities. Traffic count is provided by the Florida Department of Transportation. Moreover, the weather data is obtained from Tallahassee regional Airport weather station. This paper aims to study and establish a cause and effect relationship between the mentioned variables using different causality models and to forecast load based on the external variables. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered.