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

Journal Article · 10 June 2021 · Earth's Future

DOI: https://doi.org/10.1029/2020EF001910 · OSTI ID:1787887

Wang, Sally S. ‐C.  ^[1]; Qian, Yun  ^[1]; Leung, L. Ruby  ^[1]; Zhang, Yang  ^[2]

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1. Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USA
2. Department of Civil and Environmental Engineering Northeastern University Boston MA USA

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 several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: US Environmental Protection Agency (EPA); USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1787887
• Report Number(s): PNNL-SA--162325; e2020EF001910
• Journal Information: Earth's Future, Journal Name: Earth's Future Journal Issue: 6 Vol. 9; ISSN 2328-4277
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English