Disruptions in airline operations are not uncommon and can interrupt smooth and efficient passenger transportation, especially during extreme weather conditions and hurricanes. Airline operations can be severely affected and/or halted for the duration of these phenomena. In order to develop tools to implement proper recovery actions for different stakeholders during a disruption present in an air transportation system, prior hurricane data analysis is crucial. This work focuses on analyzing a large set of airline data during Hurricane Matthew in 2016 to obtain meaningful insights regarding the affected airports and airlines. Our analysis also predicts the number of affected airline passengers during the hurricane. The results of our study show that Orlando International Airport (MCO) and Southwest Airlines were the most affected airport and airline, respectively. Our findings further reveal that certain airline passengers were affected before and after the day of the hurricane.

When flight schedules are disrupted due to severe weather, operations must be restored to normal, expeditiously and efficiently. The restoration of airline operations during a severe weather disruption involves the analysis and interpretation of very large data sets containing flight and weather data. The principle goal of this paper is to introduce a visualization of flight and weather data. While there are currently a number of programs being used to evaluate large data sets, this paper uses Tableau and Excel visualization software. The purpose of this visualization analysis is to identify trends that could have assisted the airline decision-makers to restore operations during Hurricane Matthew. The results of this analysis provide an overview for decisionmakers to use during future severe weather events.

Food banks collect and distribute food donations to partnering agencies to help fight food insecurity. Donations come from several retail, manufacturing, and community donors and can vary significantly over time with respect to frequency, amount, and quality. Support Vector Regression has shown to have tremendous advantages over other forecasting methods but has not been previously applied to the food donation supply problem. Using data obtained from a local food bank, this study describes the following: (i) the prediction accuracy for food donations when using support vector regression; and (ii) the relationship between prediction accuracy, historical data length, and food donation data variability. Results from our study show that support vector regression has comparable results to other food donation forecasting techniques previously explored in the literature.