Document Type
Article
Publication Date
3-2022
Keywords
machine learning; random forest; fire susceptibility modeling; ordinary least squares regression; geographically weighted regression
Abstract
Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the "natural state". About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981-2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained R-squared values are significant, with 0.99 for training the model and 0.92 for the validation. The results show that the Ouachita National Forest and the Ozark Forest, in west-central and west Arkansas, respectively, have the highest susceptibility to wildfires. The southern part of Arkansas has low-to-moderate fire susceptibility, while the eastern part of the state has the lowest fire susceptibility. These new results for Arkansas demonstrate the potency of remote-sensing-based random forest in predicting fire susceptibility at the state level that can be adapted to study fires in other states and help with fire preparedness to reduce loss and save the precious environment.
Citation
Al Saim, A., & Aly, M. H. (2022). Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA. Geographies, 2 (1), 31-47. https://doi.org/10.3390/geographies2010004
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
