The FireAId initiative is a collaboration between the World Economic Forum’s Center for the Fourth Industrial Revolution, Koc Holding, the Turkish Ministry of Agriculture and Forestry, and Deloitte. As part of a pilot program in Türkiye, an interactive forest fire risk map was developed that leverages AI and machine learning algorithms. The map builds a model based on multiple data sources including historical, meteorological, and geographic data for optimal resource allocation.
According to Weforum, thanks to the successful application in Türkiye, the accuracy rate in forest fire forecasting 24 hours in advance is up to 80%. The information helps authorities prepare and respond proactively. The success of the pilot program shows how effective AI is in supporting management agencies and rescuing people, protecting property, protecting the environment, and significantly reducing damage caused by forest fires.
In addition to AI, the community is also exploring the application of other emerging technologies in wildfire management such as drones.
Climate change continues to be one of the most pressing concerns today, and its impact is clearly visible in the frequency, severity, and spread of wildfires. According to data from the National Interagency Fire Center (NIFC), approximately 3 million acres of land have been affected by fires each year since 1982. Since 2000, that number has increased to 7 million acres of land per year.
Wildfires pose a significant threat to rural areas, as they can rapidly destroy homes, infrastructure, and agricultural land. Wildlife is also burned in large numbers. In addition to destroying natural resources and property, wildfires have serious health consequences for people living in affected areas.
At the World Economic Forum Annual Meeting 2023 in Davos, the forum released a report presenting the results of the FireAId pilot in preventing forest fires. Koc Holding CEO Levent Çakıroğlu emphasized that the algorithm and source code can be made public, increasing the capabilities and impact of the FireAId initiative.
As more training data is gathered, the algorithm becomes more efficient and can make complex predictions based on many different variables. Previous AI technologies for predicting wildfires have struggled to interpret nonlinear data patterns, while new algorithms interpret them on a daily basis. Wildfires don’t always follow a pattern, and machine learning models must overcome that. Scientists are applying new techniques to optimize FireAId for unpredictable wildfires.
The global FireAId Alliance was formed based on the success of the initial pilot and aims to raise global awareness of wildfire management by leveraging the power of AI. Prioritizing AI in predicting and preventing wildfires will help save costs and increase efficiency at scale.
(According to Weforum)
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