The severity and frequency of large wildfires has increased significantly over recent years due to factors ranging from climate and weather pattern changes to increased human activities in wildland-urban interfaces. While wildfires play an important role in some forest’s natural cycle, extreme fires pose serious threats to communities and ecosystems. Frequent wildfires can disrupt, damage, and destroy infrastructure, livelihoods, lives, and properties. For example, the recent surge in US wildfires has expanded geographically with the annual burned-area estimated to be approaching 7M acres, annual wildfire economic burden to be between $394B and $893B, and annual wildfire CO2 emissions exceeding 50% of combustion emissions. In fact, greenhouse gas emissions from wildfires can wipe out years of emissions savings. This is expected to worsen worldwide, not only in fire-prone areas within the US, Canada, Australia, and southern Europe, but also in regions that haven’t had a history of extensive wildfires.
Firefighters and the research community have been studying paths to better understand and manage wildfire impacts. With rapid machine learning (ML) and high performance computing advancements, Google has explored ways to apply this technology to improve predictions for fire-risk assessment and fire resilience to help communities and authorities manage wildfires. Some examples include using AI for wildfire boundary tracking, using ML to predict fire spread from remote-sensing data, and releasing an efficient and scalable high-fidelity TPU-powered simulation framework that can reduce data scarcity for ML-based fire-prediction model development. However, a key element to effectively leveraging ML technologies for fire management is finding high-quality data, which can be difficult.
To that end, in “FireBench: A High-fidelity Ensemble Simulation Framework for Exploring Wildfire Behavior and Data-driven Modeling” we introduce a high-resolution, simulation dataset designed to advance wildfire research. FireBench enables investigations of wildfire spread behavior and the coupling between atmospheric hydrodynamics and fire physics by extending beyond just fire states to also include a comprehensive list of flow field variables in three dimensions. It also supports the development of robust and interpretable ML models by capturing the underlying dependencies between relevant variables. To provide the research community with the insights needed to mitigate the impact of wildfires, we have released the FireBench dataset on the Google Cloud Platform.