Guangzhou Institute of Science and Technology p

Predicting wildfires with the help of high-resolution deep learning

Image: Wildfire’s forecasting systems can be made more accurate by integrating artificial intelligence with weather forecasting models, according to a new study by an international research team. Their new model provides improved forecast accuracy with a lead time of up to 7 days, enabling earlier preparation and better allocation of resources to mitigating wildfire risks.
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Credit: Jin Ho-Yoon from GIST, Korea.

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 and weather forecasts to produce improved forecasts of severe fire danger for one week at finer scales (4 km x 4 km accuracy), increasing its 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, a recent 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 hazard predictions can be further improved using continuous development in both Earth system models and recent AI developments,” He adds.

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 was combined with computer models based on physical principles, we could diagnose what was going on inside this black box,” Says co-author Professor Simon Wang from Utah State University, USA. “AI-based predictions of extreme levels of fire risk are well based on strong winds and specific geographic characteristics, including high mountains and valleys in Western United States that has traditionally been difficult to solve with coarser models. “

Computational efficiency is another major advantage of this method. Conventional methods for predicting fire hazards with precise spatial resolution, 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 for the rest of the season.” says co-author Professor Kyu Sun Lim at Kyungpook National University, Korea. 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.

“In this study, AI is only being tested to predict fire hazard in the western United States. In the future, it could be applied to other types of extreme weather events or in other parts of the world,” Co-author Dr Philip J. Rush from Pacific Northwest National Laboratory and University of Washington. “The flexibility of our AI approach can help predict any weather-related feature.”

The search was published in Journal of Advances in Earth Systems Modeling On September 22, 2022.

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reference

DOI: https://doi.org/10.1029/2022MS002995

Authors: Rackhun Son1,8, Bo color what2Hailong Wang2Philp J. Rasch2,3Xie Yu (Simon)5 wang4hyung joon kim5,9,10Ji-hoon jeong6Q Sunny Lim7Jin Ho Yeon8,

Affiliations:

1 Department of Biogeochemical Integration, Max Planck Institute for Biochemistry

2 Pacific Northwest National Laboratory

3 Department of Atmospheric Sciences, University of Washington

4 Department of Plants, Soils and Climate, Utah Logan State University

5 Moon Seol Graduate School for Future Strategy, Korea Advanced Institute of Science and Technology

6 College of Earth and Environmental Sciences, Chunam National University

7 Faculty of Earth System Sciences, Kyungpook National University

8 College of Earth Sciences and Environmental Engineering, Guangzhou Institute of Science and Technology

9 Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology

10 Institute of Industrial Sciences, University of Tokyo

About Gwangju Institute of Science and Technology (GIST)

The guangzhou institute of science and technology (GIST) is a research-oriented university located in Gwangju, South Korea. Founded in 1993, GIST has become one of the most prestigious schools in South Korea. The university aims to create a strong research environment to stimulate progress in science and technology and to foster collaboration between international and domestic research programmes. With its motto “a proud creator of the science and technology of the future,” GIST has consistently earned one of the highest university rankings in Korea.

website: http://www.gist.ac.kr/

About the author

Jin Ho-yeon is Professor of Earth Science and Environmental Engineering at GIST, Korea. His group focuses on understanding and predicting extreme weather events under climate change. Professor Yun’s group also analyzes the interactions of aerosols, clouds and precipitation to understand the distribution and properties of clouds. Prior to coming to GIST, he was a Scientist (Level 3) at Pacific Northwest National Laboratory. In 2004, Professor Yun received his Ph.D. in Atmospheric Sciences from Iowa State University, USA.


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