The raging wildfires occurring all over the world have caused enormous economic damage and loss of life. Knowing when and where a large-scale fire can occur in advance can improve fire prevention and resource allocation. However, available forecasting systems provide only limited information. Furthermore, they do not provide long enough lead times for useful regional details.
Scientists have now applied a deep learning algorithm to enhance prediction of the danger of wildfires in the western United States. Researchers from South Korea and the United States have developed a hybrid method that combines artificial intelligence techniques with weather forecast To produce improved 1-week severe fire hazard predictions at more accurate scales (4 km x 4 km accuracy), increasing their usefulness in fire suppression and management.
“We have experimented with several approaches to integrate machine learning with traditional weather forecast models to improve wildfire risk predictions. This study is a huge step forward because it demonstrates the potential of such an effort to enhance fire risk prediction without the need for additional computing power,” says lead author Dr. Rackhun Son, recently received a Ph.D. from the Gwangju Institute of Science and Technology (GIST) in South Korea, who is currently at the Max Planck Institute for Biochemistry in Germany. “Fire risk predictions can be further improved using ongoing development in both Earth system models and recent advances in artificial intelligence.”
While data-driven AI methods have shown excellent capabilities to infer things, explaining why and how to come to conclusions remains a challenge. This has led to AI being classified as a black box. “But when artificial intelligence is combined with computer models based on physical principles, we can diagnose what’s going on inside this black box,” says co-author Professor Simon Wang of Utah State University. “AI-based predictions of extreme levels of fire risk are well-founded Strong wind and specific geographic characteristics, including high mountains and valleys in the western United States that are traditionally difficult to solve using coarser models.”
Computational efficiency is another major advantage of this method. Conventional methods of predicting fire hazards with precise spatial accuracy, a process called “regional minimization”, are often computationally demanding, expensive and time-consuming.
“Although comparable computational resources were required in the development phase, once the training task for the AI was completed, i.e. it was done once initially, it only took a few seconds to use this component with the weather forecasting model to produce forecasts,” says the co-author. Professor Kyu Sun Lim at Kyungpook National University, Korea, “Remaining of the Season.”
Therefore, the newly developed AI-based method with the ability to make high-accuracy accurate predictions in a shorter time was more cost-effective than traditional forecasting systems.
Co-author Dr. • Philip C. Rush from Pacific Northwest National Laboratory and University of Washington. “The flexibility of our AI approach can help predict any weather-related feature.”
