Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3407-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3407-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Jun 2023
Development and technical paper |
| 19 Jun 2023
| 19 Jun 2023
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA
A. Park Williams
Department of Geography, University of California, Los Angeles, CA, USA
Caroline S. Juang
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA
Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA
Winslow D. Hansen
Cary Institute of Ecosystem Studies, Millbrook, NY, USA
Pierre Gentine
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
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Cited
20 citations as recorded by crossref.
- Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts K. Liao et al.
- Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations V. Waeselynck & D. Saah
- Wildfire-power grid interactions: Feedback, impacts, monitoring, modeling, and mitigation strategies J. Mao et al.
- A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment N. Ejaz & S. Choudhury
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al.
- Seasonal Predictability of Vapor Pressure Deficit in the western United States M. Breeden et al.
- The Western United States Large Forest-Fire Stochastic Simulator (WULFFSS) 1.0: a monthly gridded forest-fire model using interpretable statistics A. Williams et al.
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 R. Tang et al.
- Improved distance-based detection of PM2.5 monitor deserts across the contiguous United States J. Jung et al.
- Global warming amplifies wildfire health burden and reshapes inequality J. Zhao et al.
- Understanding green house gases emission dynamics from forest fires in Thailand using predictive models F. Shahzad et al.
- Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks A. Masrur et al.
- Identifying critical fire spread to the wildland–urban interface using cellular automata and reinforcement learning J. González-Villa et al.
- Wildfire smoke exposure and mortality burden in the USA under climate change M. Qiu et al.
- Natural hazard mitigation in a rapidly changing world: Louisiana as a ‘canary in the coal mine’ for coastal flooding T. Dixon
- The fastest-growing and most destructive fires in the US (2001 to 2020) J. Balch et al.
- Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science G. Provencher Langlois et al.
- Bridging the “Last-mile Gap” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action Y. Pan et al.
- Mortality Burden from Wildfire Smoke Under Climate Change M. Qiu et al.
- A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model W. Yu et al.
20 citations as recorded by crossref.
- Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts K. Liao et al.
- Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations V. Waeselynck & D. Saah
- Wildfire-power grid interactions: Feedback, impacts, monitoring, modeling, and mitigation strategies J. Mao et al.
- A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment N. Ejaz & S. Choudhury
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al.
- Seasonal Predictability of Vapor Pressure Deficit in the western United States M. Breeden et al.
- The Western United States Large Forest-Fire Stochastic Simulator (WULFFSS) 1.0: a monthly gridded forest-fire model using interpretable statistics A. Williams et al.
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 R. Tang et al.
- Improved distance-based detection of PM2.5 monitor deserts across the contiguous United States J. Jung et al.
- Global warming amplifies wildfire health burden and reshapes inequality J. Zhao et al.
- Understanding green house gases emission dynamics from forest fires in Thailand using predictive models F. Shahzad et al.
- Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks A. Masrur et al.
- Identifying critical fire spread to the wildland–urban interface using cellular automata and reinforcement learning J. González-Villa et al.
- Wildfire smoke exposure and mortality burden in the USA under climate change M. Qiu et al.
- Natural hazard mitigation in a rapidly changing world: Louisiana as a ‘canary in the coal mine’ for coastal flooding T. Dixon
- The fastest-growing and most destructive fires in the US (2001 to 2020) J. Balch et al.
- Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science G. Provencher Langlois et al.
- Bridging the “Last-mile Gap” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action Y. Pan et al.
- Mortality Burden from Wildfire Smoke Under Climate Change M. Qiu et al.
- A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model W. Yu et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
We leverage machine learning techniques to construct a statistical model of grid-scale fire...
