  Weather disaster related emergency operations pose a great challenge to air
mobility in both aircraft and airport operations, especially when the impact is
gradually approaching. We propose an optimized framework for adjusting airport
operational schedules for such pre-disaster scenarios. We first, aggregate
operational data from multiple airports and then determine the optimal count of
evacuation flights to maximize the impacted airport's outgoing capacity without
impeding regular air traffic. We then propose a novel Neural Network (NN)
accelerated Genetic Algorithm(GA) for evacuation planning. Our experiments show
that integration yielded comparable results but with smaller computational
overhead. We find that the utilization of a NN enhances the efficiency of a GA,
facilitating more rapid convergence even when operating with a reduced
population size. This effectiveness persists even when the model is trained on
data from airports different from those under test.
