Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
Their approach draws on data about underlying environmental drivers such as ocean temperatures and land surface changes in addition to more commonly used atmospheric and socioeconomic indicators. The method allows scientists to gain a deeper understanding of the relative importance of different variables such as soil moisture and leaf area.
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