Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm
Fire is among the most dangerous and devastating natural hazards in forest ecosystems
around the world. The development of computational ensemble models for improving the
predictive accuracy of forest fire susceptibilities could save time and cost in firefighting
efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade
Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the
prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province …
around the world. The development of computational ensemble models for improving the
predictive accuracy of forest fire susceptibilities could save time and cost in firefighting
efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade
Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the
prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province …
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