Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
Open accessAbstract
Machine learning algorithms have increased tremendously in power in recent years but have yet to be fully utilized in many ecology and sustainable resource management domains such as wildlife reserve design, forest fire management and invasive species spread. One thing these domains have in common is that they contain dynamics that can be characterized as a Spatially Spreading Process (SSP) which requires many parameters to be set precisely to model the dynamics, spread rates and directional biases of the elements which are spreading. We present related work in Artificial Intelligence and Machine Learning for SSP sustainability domains including forest wildfire prediction. We then introduce a novel approach for learning in SSP domains using Reinforcement Learning (RL) where fire is the agent at any cell in the landscape and the set of actions the fire can take from a location at any point in time includes spreading North, South, East, West or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfire spread. Meanwhile, we learn an agent policy for a predictive model of the dynamics of a complex spatially-spreading process. Rewards are provided for correctly classifying which cells are on fire or not compared to satellite and other related data. We examine the behaviour of five RL algorithms on this problem: Value Iteration, Policy Iteration, Q-Learning, Monte Carlo Tree Search and Asynchronous Advantage Actor-Critic (A3C). We compare to a Gaussian process based supervised learning approach and discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modelling. We also discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modelling. We validate our approach with satellite image data of two massive wildfire events in Northern Alberta, Canada; the Fort McMurray fire of 2016 and the Richardson fire of 2011. The results show that we can learn predictive, agent-based policies as models of spatial dynamics using RL on readily available satellite images that other methods and have many additional advantages in terms of generalizability and interpretability.
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RL is used to predict the spatial progression of fire, which helps firefighters decide when to let a fire burn and when to stop it.
“In [266,267], RL is used to predict the spatial progression of fire.This helps firefighters decide when to let a fire burn and when to stop it [355].”
From Tackling climate change with machine learning (2023) · cites this paper as [267]
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- Authors
- Ganapathi Subramanian, S., & Crowley, M.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Crowley, Mark
- ORCID
- Crowley, Mark
- Venue
- Frontiers in ICT
- Publisher
- Frontiers Media SA
- Published
- 2018-04-19
- Volume
- 5
- ISSN
- 2297-198X (ISSN-L)
- OpenAlex
- W2800336546
- Cited by
- 56
- References
- 32
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 12 · 2021: 12 · 2020: 4 · 2019: 3
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Frontiers in ICT (publishedVersion, cc-by)
- Funders
- Mitacs
- University of Waterloo
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- wildfire spread prediction, satellite imagery, Markov decision process, wildfire management, Gaussian process comparison
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