Year: 2020
Type: preprint
Abstract: Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.
Source: arXiv (Cornell University)
Authors: Huot, Fantine, Hu, R. Lily, Ihme, Matthias, Wang, Qing, Burge, John, Lu, Tianjian, Hickey, Jason, Chen, Yi-Fan, Anderson, John
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: green
OpenAlex ID: W3092943061