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Publication Date
9 June 2020

Analytical Framework and Machine Learning Techniques Used to Quantify and Predict Seasonal Variation in African Fire

Understanding and predicting seasonal changes of African fire.
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The seasonal environmental drivers and predictability of African fire were investigated using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) combined with machine learning techniques (MLTs).


We provide the first quantitative measures of the strength of the drivers underlying seasonal changes in African fire. We describe a powerful regional diagnostic and prediction framework that can be generalized for building a global fire early-warning system.


Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of SGEFA and MLTs. The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning conditions and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

Point of Contact
Jiafu Mao
Oak Ridge National Laboratory (ORNL)
Funding Program Area(s)