Predicting burned areas: a machine learning approach

Abstract

Wildfires in Portugal are a recurrent and growing threat, particularly during the hotter months. In response to the increasing risks posed by wildfires, this study explores the use of machine learning models to forecast fire occurrences and burned areas across six districts in Continental Portugal. Using historical fire data from 2001 to 2023, combined with meteorological information such as temperature, precipitation, and humidity, we developed several predictive models using Python. The districts of Lisboa, Porto, Aveiro, Setúbal, Viseu, and Braga were selected based on a high concentration of insurance policies, making the predictions particularly relevant to the company. Our results indicate that while some districts demonstrate a higher likelihood of fires occurring, the predicted burned areas remain modest. Furthermore, certain models underperformed, likely due to incomplete or insufficient data, particularly meteorological records. This research highlights the potential for machine learning to enhance fire risk forecasting and introduces innovative methods for the insurance sector to better manage wildfire-related risks.info:eu-repo/semantics/publishedVersio