Photo caption:听Wildfire research programme experimental burn.
Developed by an international team led by Dr Alberto Ardid, a research engineer in Civil and Natural Resources Engineering at Te Whare W膩nanga o Waitaha | 91制片厂 (UC), the new tool uses machine learning (a type of artificial intelligence) to analyse ever-changing weather data.听
Dr Ardid says the frequency and intensity of wildfires, such as the destructive blazes seen in California recently, is increasing due to climate change and there鈥檚 an urgent need for more effective fire management strategies to protect lives, property, and ecosystems.
鈥淎ccurate and timely wildfire danger forecasting is necessary for preparedness and response, enabling efficient resource allocation and mitigation efforts. Weather conditions can change dramatically within hours, potentially leading to sudden wildfire outbreaks,鈥 he says. 鈥淲e are developing new tools that are sensitive to these hourly weather conditions which can help identify high fire hazard conditions.鈥澨
His team has developed a unique AI-based system that uses readily available weather data to predict wildfire danger in real-time, providing a powerful tool to inform timely decision-making.听
鈥淥ur AI model analyses weather data every 30 minutes, capturing dynamic weather patterns that can contribute to sudden wildfire outbreaks,鈥 Dr Ardid says. 鈥淭his approach offers a cost-effective solution for communities and agencies to enhance their wildfire prediction and response capabilities, contributing to community safety and resilience in the face of increasing wildfire danger.鈥
The model outputs a prediction of the likelihood of fire in the next few days, continuously updated with new meteorological data. The real-time monitoring system uses existing data and infrastructure making it cost-effective in regions with limited resources.听
鈥淲e hope this research will complement current monitoring techniques and advance wildfire management, offering a valuable tool for mitigating the increasing threat of wildfires and protecting communities,鈥 Dr Ardid says.
The AI model was developed and tested using historical weather and fire data from Queensland, Australia, and achieved a 47% improvement in predicting critical pre-fire conditions compared to the existing Forest Fire Danger Index.听听
, a senior lecturer in UC鈥檚 Civil and Natural Resources Engineering department, collaborated on the research which was published in the today. He says the system鈥檚 early and accurate warnings would facilitate faster evacuations.听
鈥淚t would also improve fire-fighting strategies and allow fire management agencies to allocate their resources more effectively,鈥 Dr Valencia-Correa says. 鈥淭his enhanced predictive capability could potentially save lives and lessen the adverse impacts of wildfires on communities and ecosystems.鈥
This project was recognised recently for its potential impact and innovation, being selected among the top 10 finalists for the for early-career scientists.
The research team also includes UC School of Earth and Environment Associate Professor , UC Civil and Natural Resources Engineering , and fire ecologist Shana Gross, from Scion.听
The other collaborators are Anthony Power, an Australian bush fire consultant, and Professor Matthias Boer from Western Sydney University.听
UC Research Engineer Dr Alberto Ardid (right) and Dr Andres Valencia-Correa, a senior lecturer in UC鈥檚 Civil and Natural Resources Engineering department, are part of a team developing a new AI-powered tool to forecast wildfire danger.
