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Quantifying the drivers and predictability of seasonal changes in African fire

Earth Sciences

Quantifying the drivers and predictability of seasonal changes in African fire

Y. Yu, J. Mao, et al.

Explore the groundbreaking research conducted by Yan Yu and colleagues, revealing how seasonal environmental factors like sea-surface temperature and soil moisture influence African fire predictability. Their innovative use of Stepwise Generalized Equilibrium Feedback Assessment combined with machine learning techniques offers a robust method for forecasting fires a month in advance.

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Playback language: English
Abstract
This study investigates the seasonal environmental drivers and predictability of African fire using Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). Sea-surface temperature, soil moisture, and leaf area index are identified as dominant drivers, regulating burning conditions and fuel supply. The combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance.
Publisher
Nature Communications
Published On
Jun 09, 2020
Authors
Yan Yu, Jiafu Mao, Peter E. Thornton, Michael Notaro, Stan D. Wullschleger, Xiaoying Shi, Forrest M. Hoffman, Yaoping Wang
Tags
African fire
seasonal environmental drivers
machine learning techniques
predictability
Stepwise Generalized Equilibrium Feedback Assessment

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