Wildfire Prediction in the United States Using Time Series Forecasting Models

Abstract

Wildfires are a widespread phenomenon that affects every corner of the world with the warming climate. Wildfires burn tens of thousands of square kilometres of forests and vegetation every year in the United States alone with the past decade witnessing a dramatic increase in the number of wildfire incidents. This research aims to understand the regions of forests and vegetation across the US that are susceptible to wildfires using spatiotemporal kernel heat maps and, forecast these wildfires across the United States at country-wide and state levels on a weekly and monthly basis in an attempt to reduce the reaction time of the suppression operations and effectively design resource maps to mitigate wildfires. We employed the state-of-the-art Neural Basis Expansion Analysis for Time Series (N-BEATS) model to predict the total area burned by wildfires by several weeks and months into the future. The model was evaluated based on forecasting metrics including mean-squared error (MSE)., and mean average error (MAE). The N-BEATS model demonstrates improved performance compared to other state-of-the-art (SOTA) models, obtaining MSE values of 116.3, 38.2, and 19.0 for yearly, monthly, and weekly forecasting, respectively.