Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation.

ABSTRACT: Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in s

SUBMITTER: Wang SS 

PROVIDER: S-EPMC8243942 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature