The Next Frontier in Fighting Wildfires: FireAId Pilot and Scaling

The report examines the escalating threat of wildfires driven by climate change and proposes the use of AI and machine learning to improve prediction, containment, and response. It highlights the FireAId initiative, a multistakeholder collaboration between the World Economic Forum, Deloitte, and Koç Holding. The document details a pilot project in Türkiye that developed a dynamic wildfire risk map and a resource allocation model to optimize firefighting logistics. It also outlines a five-stage vision for wildfire risk management, ranging from data consolidation to autonomous systems, while identifying critical barriers to scaling such technology, including data accessibility, computational costs, and the need for international standardization.

• Wildfire frequency and severity are increasing globally due to climate change, with the World Meteorological Organization projecting a global increase in extreme fires of up to 30% by 2050 and up to 50% by the end of the century.
• In the United States, the average annual burned area has more than doubled in just over 20 years, rising from 3 million acres in the 1983-2000 average to 7 million acres since 2000.
• The FireAId initiative aims to facilitate the use of AI for wildfire prediction and prevention to improve the efficiency of efforts that cost approximately $50 billion annually on a global average.
• The KoçDigital pilot in Türkiye developed a binary classification model to create a dynamic risk map for the South Aegean and West Mediterranean regions, achieving recall values of 0.8 for fire and 0.5 for no-fire events during testing.
• The Türkiye pilot also implemented a resource allocation model based on a minimum cost network flow problem to optimize the relocation of firefighting teams between 332 subdistrict directorates.
• Deloitte's proposed solution approach utilizes a modified MetNet architecture (CNN-LSTM) and the FourCastNet model to provide 'nowcasting' and longer-term weather-based wildfire predictions.
• A proposed five-stage framework for wildfire risk management moves from problem definition and data consolidation to modelling, the creation of a digital twin for simulation, and eventually autonomous wildfire risk management.
• Significant barriers to scaling AI wildfire solutions include the high cost of high-resolution satellite imagery, the lack of global data standards, and the computational intensity of spatiotemporal forecasting.
• Human behavior is a primary driver of wildfire ignition, with approximately 80% of fires in Türkiye caused by human activity, creating a significant challenge for predictive modelling.

APA

World Economic Forum (2023). The Next Frontier in Fighting Wildfires: FireAId Pilot and Scaling. https://www3.weforum.org/docs/WEF_The_Next_Frontier_Wildfire_Fighting_2023.pdf

Chicago

World Economic Forum. The Next Frontier in Fighting Wildfires: FireAId Pilot and Scaling. 2023. https://www3.weforum.org/docs/WEF_The_Next_Frontier_Wildfire_Fighting_2023.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=The Next Frontier in Fighting Wildfires: FireAId Pilot and Scaling |date=January 2023 |url=https://www3.weforum.org/docs/WEF_The_Next_Frontier_Wildfire_Fighting_2023.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2023next, author = {{World Economic Forum}}, title = {{The Next Frontier in Fighting Wildfires: FireAId Pilot and Scaling}}, institution = {World Economic Forum}, year = {2023}, month = jan, url = {https://www3.weforum.org/docs/WEF_The_Next_Frontier_Wildfire_Fighting_2023.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }