Artificial intelligence to predict forest fires: a breakthrough from Israel

The threat of wildfires has intensified in recent decades as a direct consequence of climate change . These extreme events not only destroy ecosystems and biodiversity, but also put entire communities and critical infrastructure at risk and contribute to greenhouse gas emissions. In this context, a group of researchers from Tel Aviv University has developed a new method based on artificial intelligence (AI) that promises to revolutionize the way these natural disasters are predicted.

The study, published in Nature's npj Natural Hazards journal, proposes a completely different approach than traditional weather indices , adapting risk calculations to the specific conditions of each country. This increases prediction accuracy from 70 to 86 percent, a significant leap that can make a difference in preventing wildfires .

Until now, most fire risk indices were designed in regions such as Australia, Canada, and the United States , countries with extensive experience in wildfire management. While these tools have proven effective in their native territories, their performance is significantly reduced when applied to other environments with different climates, vegetation, and land uses.

For example, when analyzing the accuracy of the three major indices across 160 countries, researchers found that the Canadian index was the most reliable, but only achieved a 70 percent accuracy rate. This accuracy gap is critical, as it determines the ability of emergency services to respond quickly and prevent the spread of a fire.

To overcome these limitations, the Israeli team turned to a genetic algorithm capable of calibrating the indices according to the local characteristics of each country. This initial improvement raised accuracy to 80 percent. However, the real breakthrough came with the implementation of a nation-specific artificial intelligence model .

The result was transformed into a simple decision tree , easy to apply by field teams and local authorities, with an 86 percent effectiveness rate. This tool not only makes it possible to better predict where and when a fire might occur, but also offers clear criteria tailored to each area.

The ability to make more accurate predictions has a direct impact on public safety and environmental protection . According to researchers, this method will allow emergency services to improve early warnings , optimize resource allocation , and significantly reduce fire-related damage.

Furthermore, public policymakers will be able to use this approach to design more effective preventive strategies, tailored to the specificities of each region. In a world experiencing more intense and prolonged heat seasons, having personalized prediction tools is a fundamental step toward protecting both human communities and natural ecosystems.

Beyond its immediate application, the researchers emphasize that this AI-based model lays the groundwork for the development of even more sophisticated future systems . The combination of meteorological data, land-use information, and ignition pattern analysis paves the way for creating global platforms capable of predicting fires with unprecedented precision.

While no technology can completely eliminate risk, advances in AI applied to disaster prevention represent one of the best tools available to address the challenges of climate change. Israel's case demonstrates that scientific innovation can make a difference in the fight against increasingly frequent and devastating natural phenomena.

Forest fires will continue to be a growing threat in the 21st century, but science offers new hope. The model developed by Israeli researchers not only improves the prediction and prevention of these disasters, but also inaugurates a new era in the way we address environmental risks. Adapting technology to local conditions is key to protecting lives and ecosystems and reducing the impact of global warming. With more precise and customized tools, the future could be less vulnerable to fire and more resilient to climate change.