Wildfires are becoming larger and affecting more and more communities around the world, often resulting in large-scale devastation. Just this year, communities have experienced catastrophic wildfires in Greece, Maui, and Canada to name a few. While the underlying causes leading to such an increase are complex — including changing climate patterns, forest management practices, land use development policies and many more — it is clear that the advancement of technologies can help to address the new challenges.

At Google Research, we’ve been investing in a number of climate adaptation efforts, including the application of machine learning (ML) to aid in wildfire prevention and provide information to people during these events. For example, to help map fire boundaries, our wildfire boundary tracker uses ML models and satellite imagery to map large fires in near real-time with updates every 15 minutes. To advance our various research efforts, we are partnering with wildfire experts and government agencies around the world.

Today we are excited to share more about our ongoing collaboration with the US Forest Service (USFS) to advance fire modeling tools and fire spread prediction algorithms. Starting from the newly developed USFS wildfire behavior model, we use ML to significantly reduce computation times, thus enabling the model to be employed in near real time. This new model is also capable of incorporating localized fuel characteristics, such as fuel type and distribution, in its predictions. Finally, we describe an early version of our new high-fidelity 3D fire spread model.

Current state of the art in wildfire modeling

Today’s most widely used state-of-the-art fire behavior models for fire operation and training are based on the Rothermel fire model developed at the US Forest Service Fire Lab, by Rothermel et al., in the 1970s. This model considers many key factors that affect fire spread, such as the influence of wind, the slope of the terrain, the moisture level, the fuel load (e.g., the density of the combustible materials in the forest), etc., and provided a good balance between computational feasibility and accuracy at the time. The Rothermel model has gained widespread use throughout the fire management community across the world.

Various operational tools that employ the Rothermel model, such as BEHAVE, FARSITE, FSPro, and FlamMap, have been developed and improved over the years. These tools and the underlying model are used mainly in three important ways: (1) for training firefighters and fire managers to develop their insights and intuitions on fire behavior, (2) for fire behavior analysts to predict the development of a fire during a fire operation and to generate guidance for situation awareness and resource allocation planning, and (3) for analyzing forest management options intended to mitigate fire hazards across large landscapes.  These models are the foundation of fire operation safety and efficiency today.

However, there are limitations on these state-of-the art models, mostly associated with the simplification of the underlying physical processes (which was necessary when these models were created). By simplifying the physics to produce steady state predictions, the required inputs for fuel sources and weather became practical but also more abstract compared to measurable quantities.  As a result, these models are typically “adjusted” and “tweaked” by experienced fire behavior analysts so they work more accurately in certain situations and to compensate for uncertainties and unknowable environmental characteristics. Yet these expert adjustments mean that many of the calculations are not repeatable.

To overcome these limitations, USFS researchers have been working on a new model to drastically improve the physical fidelity of fire behavior prediction. This effort represents the first major shift in fire modeling in the past 50 years. While the new model continues to improve in capturing fire behavior, the computational cost and inference time makes it impractical to be deployed in the field or for applications with near real-time requirements. In a realistic scenario, to make this model useful and practical in training and operations, a speed up of at least 1000x would be needed.

Machine learning acceleration

In partnership with the USFS, we have undertaken a program to apply ML to decrease computation times for complex fire models. Researchers knew that many complex inputs and features could be characterized using a deep neural network, and if successful, the trained model would lower the computational cost and latency of evaluating new scenarios. Deep learning is a branch of machine learning that uses neural networks with multiple hidden layers of nodes that do not directly correspond to actual observations. The model’s hidden layers allow a rich representation of extremely complex systems — an ideal technique for modeling wildfire spread.

We used the USFS physics-based, numerical prediction models to generate many simulations of wildfire behavior and then used these simulated examples to train the deep learning model on the inputs and features to best capture the system behavior accurately. We found that the deep learning model can perform at a much lower computational cost compared to the original and is able to address behaviors resulting from fine-scale processes. In some cases, computation time for capturing the fine-scale features described above and providing a fire spread estimate was 100,000 times faster than running the physics-based numerical models.

This project has continued to make great progress since the first report at presentation at ICFFR 2022 and the USFS Fire Lab's project page provides a glimpse into the ongoing work in this direction. Our team has expanded the dataset used for training by an order of magnitude, from 40M up to 550M training examples. Additionally, we have delivered a prototype ML model that our USFS Fire Lab partner is integrating into a training app that is currently being developed for release in 2024.

Google researchers visiting the USFS Fire Lab in Missoula, MT, stopping by Big Knife Fire Operation Command Center.

Fine-grained fuel representation

Besides training, another key use-case of the new model is for operational fire prediction. To fully leverage the advantages of the new model’s capability to capture the detailed fire behavior changes from small-scale differences in fuel structures, high resolution fuel mapping and representation are needed. To this end, we are currently working on the integration of high resolution satellite imagery and geo information into ML models to allow fuel specific mapping at-scale. Some of the preliminary results will be presented at the upcoming 10th International Fire Ecology and Management Congress in November 2023.

Future work

Beyond the collaboration on the new fire spread model, there are many important and challenging problems that can help fire management and safety. Many such problems require even more accurate fire models that fully consider 3D flow interactions and fluid dynamics, thermodynamics and combustion physics. Such detailed calculations usually require high-performance computers (HPCs) or supercomputers.

These models can be used for research and longer-term planning purposes to develop insights on extreme fire development scenarios, build ML classification models, or establish a meaningful “danger index” using the simulated results. These high-fidelity simulations can also be used to supplement physical experiments that are used in expanding the operational models mentioned above.

In this direction, Google research has also developed a high-fidelity large-scale 3D fire simulator that can be run on Google TPUs. In the near future, there is a plan to further leverage this new capability to augment the experiments, and to generate data to build insights on the development of extreme fires and use the data to design a fire-danger classifier and fire-danger index protocol.

An example of 3D high-fidelity simulation. This is a controlled burn field experiment (FireFlux II) simulated using Google’s high fidelity fire simulator.

Acknowledgements

We thank Mark Finney, Jason Forthofer, William Chatham and Issac Grenfell from US Forest Service Missoula Fire Science Laboratory and our colleagues John Burge, Lily Hu, Qing Wang, Cenk Gazen, Matthias Ihme, Vivian Yang, Fei Sha and John Anderson for core contributions and useful discussions. We also thank Tyler Russell for his assistance with program management and coordination.