AI Model Predicts Lightning Wildfires With 90% Accuracy, Transforming Global Fire Response

Harnessing AI to Outpace Fire: A new Israeli-developed machine learning model accurately forecasts lightning-ignited wildfires, promising a game-changing leap in emergency preparedness as climate-driven fire threats intensify worldwide.

Research: Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models. Image Credit: Tinny Photo / Shutterstock

A groundbreaking new artificial intelligence (AI) model developed by Israeli researchers promises to revolutionize wildfire prediction. The model, which can predict where and when lightning strikes are most likely to cause wildfires, achieves over 90% accuracy, a first in wildfire forecasting. The model focuses particularly on lightning-induced blazes, which are becoming increasingly common due to climate change.

Dr. Oren Glickman and Dr. Assaf Shmuel from the Department of Computer Science at Bar-Ilan University, in collaboration with experts from Ariel and Tel Aviv Universities, utilized seven years of high-resolution global satellite data, alongside detailed environmental factors like vegetation, weather patterns, and topography, to map and predict lightning-induced wildfire risks on a worldwide scale. Their research was recently published in the journal Scientific Reports.

What makes the research by Dr. Glickman, Dr. Shmuel, and their colleagues so significant is their ability to predict lightning-induced wildfires with remarkable precision. The AI model outperforms traditional fire danger indices by taking a global, data-driven approach. It integrates data from satellites, weather systems, and environmental factors to assess the likelihood of lightning-induced fires, overcoming the limitations of regional and data-restricted models.

The model was rigorously tested using wildfire data from 2021 and showed an unprecedented accuracy rate of over 90%, a level of precision that could transform emergency response and disaster management worldwide.

As climate change accelerates, extreme weather events, such as lightning storms, hot and dry conditions, and shifting ecosystems, contribute to more frequent and intense wildfires. While human activity is often responsible for igniting many fires, lightning remains one of the most unpredictable and deadly causes, particularly in remote regions. These fires can smolder undetected for days, only to erupt into uncontrollable infernos before firefighters can respond. The catastrophic wildfires that ravaged Northern California in August 2020 were sparked by lightning strikes, burning more than 1.5 million acres and claiming dozens of lives.

With an improved ability to predict lightning fires, meteorological services, fire departments, and emergency planners can respond earlier, smarter, and more effectively, potentially saving lives and protecting ecosystems. This model also addresses a key gap in existing wildfire prediction models. While many models are effective for fires caused by human activity, they struggle to predict lightning-induced fires, which behave very differently and often start in hard-to-reach areas.

While the AI model is not yet integrated into real-time forecasting systems, its development marks a critical step forward in wildfire prediction. Dr. Shmuel notes, "With the growing implications of climate change, new modeling tools are required to better understand and predict its impacts; machine learning holds significant potential to enhance these efforts."

The team's new machine learning models have the potential to predict lightning-ignited wildfires worldwide, offering a powerful tool for fire mitigation and response. With an ever-increasing risk of wildfires driven by climate change, early detection and prediction are essential for protecting forests, wildlife, and human communities from the devastating effects of these fires.

"We are at a critical moment in understanding the complexities of wildfire ignitions," said Dr. Glickman, from Bar-Ilan University's Department of Computer Science. "Machine learning offers the potential to revolutionize how we predict and respond to lightning-ignited wildfires, providing insights that could save lives and preserve ecosystems."