AI System Could Predict Wildfires Faster and Improve Emergency Response
AI System Could Predict Wildfires Faster and Improve Emergency Response
Researchers from University of Canterbury in New Zealand have developed an artificial intelligence–powered wildfire forecasting system that could detect dangerous fire conditions earlier and help reduce the cost of wildfire response. The study, led by civil and environmental engineering lecturer Alberto Ardid, was published in the International Journal of Wildland Fire.
The AI system uses machine learning to analyze weather station data and identify patterns that commonly appear before wildfires ignite. Unlike many traditional warning systems that update once per day, the new model can update forecasts every 30 minutes, allowing authorities to monitor fire risks in near real time.
Wildfire threats are increasing worldwide as climate change brings hotter and drier conditions. According to Dr. Ardid, fire weather can change rapidly within hours, making faster warning systems essential for improving emergency response.
The latest research builds on earlier work conducted in 2025 that tested the concept in Queensland, Australia. The new study expanded the testing to several locations with different climates, including Brisbane, the Sunshine Coast, and Hobart. Results showed the AI model consistently outperformed the standard Fire Behaviour Index used in Australia’s official fire danger rating system.
Using more than 60 years of historical weather and wildfire data, researchers found that the AI system improved forecasting performance by 10% to 30 percent. The study also revealed potential economic benefits, suggesting the model could significantly reduce costs by lowering the number of missed fires and unnecessary false alarms.
The need for improved wildfire prediction is critical. During Australia’s devastating 2019–2020 bushfire season, nearly 17 million hectares burned, more than 1.5 billion animals were killed, insurance claims reached $1.9 billion, and 33 people lost their lives.
Because the AI model relies on existing weather station networks, researchers say it could be implemented widely without the need for new infrastructure. The system could also potentially be adapted for countries like New Zealand, where similar monitoring networks are already in place.
Experts believe faster, data-driven forecasting tools like this AI system could help fire agencies respond more quickly, allocate resources more effectively, and reduce the environmental and economic damage caused by large wildfires.
