A machine learning workflow for hurricane prediction
Albert Njoroge Kahira, Leonardo Bautista-Gomez, Rosa M. Badia · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2018
The Atlantic hurricane season runs from June 1st to November causing massive destruction and loss of life.In 2017, 17 named storms hit the Atlantic causing destruction worth an estimated $316 million and at least 464 fatalities.Meteorologists, by studying previous weather data, predict the expected number of hurricanes in the season.These predictions help authorities prepare for disasters and over the years, better predictions have minimized loss of life and property.However, these predictions rely on human expertise and are often extremely complex due to the thousands of parameters involved and the chaotic nature of weather.We propose and implement a machine learning model based on deep neural networks to predict the number of hurricanes in the hurricane season.We train the model with more than 100 years of climate data and test it with 5 years.Early results achieve an accuracy of 73% in predicting the number of hurricanes.