Impact of Geophysical and Anthropogenic Factors on Wildfire Size: A Spatiotemporal Data-Driven Risk Assessment Approach Using Statistical Learning

Abstract

Wildfire spread is a stochastic phenomenon driven by a multitude of geophysical and anthropogenic factors. In this study, we propose a spatiotemporal data-driven risk assessment framework to understand the effect of various geophysical/anthropogenic factors on wildfire size, leveraging a systematic machine learning approach. We apply this framework in the state of California—the most vulnerable US state to wildfires. Using county-level annual wildfire data from 2001-2015, and various geophysical (e.g., landcover, wind, surface temperature) and anthropogenic features (e.g., population density, housing type), we trained, tested, and validated a suite of ensemble tree-based learning algorithms to identify and evaluate the key factors associated with wildfire size. The extreme gradient boosting (XGBoost) algorithm outperformed all the other models in terms of generalization performance, categorization of important features, and risk performance. We found that standard deviations of meteorological variables with long-tailed distributions play a key role in predicting wildfire size. Specifically, the top ten factors associated with high risk of larger wildfires include larger standard deviations of surface temperature and vapor pressure deficit, higher wind gust, more grassy and barren land covers, lower night-time boundary layer height and higher population density. Our proposed risk assessment framework will help federal/state decision-makers to adequately plan for wildfire risk mitigation and resource allocation strategies.

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

“…Tese statistics show that Turkey's afected areas due to forest fres are increasing with time. Research reveals that anthropogenic factors [4,5] and climate change [6][7][8] play a critical role in the frequency of wildfre occurrence and increase in afected areas. Wildfres are not only responsible for the mass destruction of forests but they also have several adverse efects on the natural environment, such as increased erosion risk [9][10][11][12], poor water quality [9], changes in land use [13], and elimination of wildlife [14].…”

Detection of burned areas with extreme gradient boosting algorithm in a multi-temporal setting (MBAGB)

“…Satellite products are based on a great variety of algorithms with different efficiencies depending on image resolutions and ecosystem variety, yet recently the fast evolution of deep learning and machine learning methods have enabled to improve the detection of burned areas [11] or the study of wildfire spread patterns [12]. Commonly, a burned vegetation index is created for allowing the identification of burned and unburned areas defined on different thresholds after filtering low-quality pixels, and then combining this information with active fires.…”