An AI hybrid predictive tool for extreme hurricane forecasting

Abstract

Hurricanes, and more generally tropical cyclones, are among the most destructive natural hazards, and are arguably changing under climate change influences. Applying the power of AI to predict the extreme behavior of these events could be key to helping minimize hurricane damage. AI tools are a significant opportunity to: 1) identify non-linear relationships between changing hurricane-related characteristics and tropical storm intensification, and 2) anticipate responses to these changes. Another key part of this AI-based system is uncertainty quantification for decision-making processes. In this context, we present an improved ML hybrid model for predicting the development of extreme hurricane events, which includes effective information on spatio-temporal evolution variations of structural parameters extracted from IR satellite images. This approach, which combines Convolutional Neural Networks (CNNs) and a Random Forest (RF) classification framework, has been trained/tested with data from 1995 over the North Atlantic and NorthEast Pacific regions. Results from the CNN-RF model shows a performance of 80% or better considering lead-times of up to three days ahead (every 6 hours). With the proposed configuration, the overall precision has increased by at least 8%. This model could be yet further improved with the inclusion of new variables linked to environmental factors to be progressively explored.