BIO

I am a sophomore student, from Jacksonville, majoring in Computer Science. I enjoy cooking and painting in my free time as well as spending time with my friends and family.

Prediction of Hurricane Paths through Neural Networks

Abstract

Background and purpose: Studies suggest that there are alternative methods to precisely predict hurricane trajectories. We examined past hurricanes to determine a pattern and cross-reference these neural-networks to create a new system of predicting through coding via python.
Methods: Information was gathered from the past 20 years in the panhandle area of Florida. We observed and analyzed hurricane hits and considered wind patterns on the date and location of the event. Data was also gathered from each big hurricane that happened in the area specifically from the past 10 years such as Hurricane Michael and Irma. Information being collected is later converted into data points to develop a pattern including the date of landfall, the strength of the hurricane, diameter, etc.
Results: Not yet determined (still in progress).
Conclusions: In progress. Idea is that when peers are done implementing perception in regard to the hurricane trajectories through the python coding system, we can try different algorithms and begin to input data points from past hurricane events.

Keywords: Neural Networks, Machine Learning, Hurricanes