DOI: 10.3390/fire6080319 ISSN:

A Machine-Learning Approach to Predicting Daily Wildfire Expansion Rate

Assaf Shmuel, Eyal Heifetz
  • Earth and Planetary Sciences (miscellaneous)
  • Safety Research
  • Environmental Science (miscellaneous)
  • Safety, Risk, Reliability and Quality
  • Building and Construction
  • Forestry

Accurate predictions of daily wildfire growth rates are crucial, as extreme wildfires have become increasingly frequent in recent years. The factors which determine wildfire growth rates are complex and depend on numerous meteorological factors, topography, and fuel loads. In this paper, we have built upon previous studies that have mapped daily burned areas at the individual fire level around the globe. We applied several Machine Learning (ML) algorithms including XGBoost, Random Forest, and Multilayer Perceptron to predict daily fire growth rate based on meteorological factors, topography, and fuel loads. Our best model on the entire dataset obtained a 1.15 km2 MAE. The ML model obtained a 90% accuracy when predicting whether a fire’s growth rate will increase or decrease the following day, compared to 61% using a logistic regression. We discuss the central factors that determine wildfire growth rate. To the best of our knowledge, this study is the first to perform such analyses on a global dataset.

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