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Israeli Researchers Develop AI Model to Predict Lightning-Induced Wildfires with 90% Accuracy

This innovation could transform global wildfire prevention efforts, helping governments, emergency responders, and environmental agencies better allocate resources and mitigate disaster risks

wildfire

Researchers at Bar-Ilan University have developed a groundbreaking AI model capable of predicting lightning-induced wildfires with unprecedented accuracy. This cutting-edge technology marks a major breakthrough in wildfire prevention and climate risk management.

As lightning-triggered wildfires become more frequent due to climate change, this AI-driven model provides a highly precise forecast of when and where lightning strikes are most likely to ignite fires—achieving over 90% accuracy, a first in wildfire prediction technology.

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This innovation could transform global wildfire prevention efforts, helping governments, emergency responders, and environmental agencies better allocate resources and mitigate disaster risks.

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 global scale. Their research was recently published in Scientific Reports.

The key significance of Dr. Glickman, Dr. Shmuel, and colleagues’ work is their development of an AI model that predicts lightning-induced wildfires with unprecedented accuracy. This model’s global, data-driven approach, which combines satellite, weather, and environmental data, enables it to outperform traditional fire danger indices that are often limited by regional focus and restricted datasets.

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.

Climate change is exacerbating extreme weather events, leading to more frequent and intense wildfires. Although human activity is a significant factor, lightning remains a particularly dangerous and unpredictable cause, especially in remote areas where fires can smolder for days before becoming uncontrollable. The devastating 2020 Northern California wildfires, ignited by lightning, underscore this threat, burning over 1.5 million acres and causing numerous deaths. Therefore, the improved ability to predict lightning-induced fires is crucial.

Advanced models allow for earlier, more intelligent, and effective responses from meteorological services, fire departments, and emergency planners, potentially saving lives and protecting ecosystems. Furthermore, these models address a critical weakness in existing wildfire prediction systems, which often struggle to accurately forecast lightning-ignited fires that behave differently and start in remote locations.

The scientists added that, while the AI model is not yet integrated into real-time forecasting systems, its development marks a critical step forward in wildfire prediction.

As 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 new machine learning models developed by the team 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.”

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