Algorithms for Modeling, Mapping, and Mitigating Wildfire Risk

Government agencies have adopted various algorithms to combat the wildfires that have ravaged much of California and the Pacific Northwest this past summer. Just like COVID-related government algorithms, wildfire-related algorithms present a variety of approaches to help understand and address a widespread issue.  

Many of the wildfire-related government algorithms that we found at Algorithm Tips aim to identify and model wildfires in real-time. For example, Sonoma County, California, incorporated a computer vision-based tool called ALERTWildfire for detecting fires in preparation for this year’s wildfire season. NASA’s IMERG algorithm uses satellite data to estimate precipitation over the majority of the Earth’s surface, and in turn, inform models of fire conditions. Similarly, the National Integrated Drought Information System wildfire conditions map visualizes active wildfires alongside current drought conditions across the country. 

Other algorithms make predictions about wildfire risk. For instance, the Oregon Wildfire Risk Explorer creates custom maps, detailed summaries, and risk reports based on public wildfire data. At a national scale, the National Fire Protection Association (NFPA) has been piloting a digital community risk assessment tool that similarly helps visualize fire and accident risks within a given community through customized maps, charts, and graphs based on various data related to geography, economics, demographics, and infrastructure.

These wildfire-related algorithms are important given the serious and widespread threats that wildfires pose. The 2021 Dixie Fire burned nearly a million acres, for example. In 2019, wildfires caused an estimated $4.5 billion in damages in California and Alaska, and since 2000, 15 forest fires in the U.S. have caused at least $1 billion in damages each. Recent decades have seen an increase in forest fire activity over the western U.S. and Alaska, and as climate change continues to exacerbate natural hazards like wildfires, these algorithms will only continue to grow in importance. 

Risk modeling algorithms are especially important as they inform other decision-making processes. The aforementioned algorithm from Oregon informs professional planners as they update Community Wildfire Protection Plans, for example. Similarly, Savannah, Georgia, one of the communities in the NFPA pilot program, explained how the NFPA dashboard will help it make data-informed decisions about local fire prevention efforts. Furthermore, as in the case of flooding, fire risk assessments also inform insurance policies

As journalists cover the development of firefighting efforts, they can investigate the new technologies, such as computer vision and artificial intelligence, that government agencies have adopted to combat wildfires. Journalists can also investigate the various fire-related risk assessments guiding policy and insurance decisions as they are made in response to active wildfires. 

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