Forest Wildfire Management

Wildfire management is the study of managing, predicting, and mitigating risk of forest wildfires.

DOMAINS | forest-management | forest-wildfire | computational-sustainability

As part of the more general domain of Sustainable Forest Management, the broad task of Forest Fire Management which presents a number of unique challenges which push the boundaries of what is possible with existing AI/ML algorithms. These include the importance of considering:

Using multi-modal remote sensing data (LiDAR, drones, satellite, etc.) to build generative models of forests to improve modelling of fire spread, carbon sequestration, forest management
predicting daily fire spread from historical data automatically by combining information from weather, satellite hotspot detection and other available GIS information
multiple spatial-temporal scales at all times
tradeoffs between individual and social good
paucity of supervised training data
coordinating decisions amongst a large number of agents (distributed nationwide) without frequent, extensive communication.

From 2019 to 2026 the lab has been part of the NSERC Canada Wildfire Strategic Network.

Completed Theses Related to Forest Wildfire Management

MASc Thesis

Canada Wildfire Next-Day Spread Prediction Tools Using Deep Learning

Xiang Fang.

UWSpace Thesis Repository, University of Waterloo, Aug, 2024.

Abs URL

Wildfires have become a pressing issue globally, with their increasing frequency and intensity causing significant environmental, economic, and human impacts. Traditional wildfire prediction methods, while useful, often fall short in time complexity or simulation on heterogeneous landscapes. This thesis explores the application of deep learning models, especially convolutional networks, to improve the accuracy and reliability of wildfire spread predictions. By leveraging advanced machine learning techniques, this research aims to enhance the current prediction capabilities and provide better tools for Canadian wildfire management and mitigation. Utilizing a comprehensive dataset from various sources, this thesis integrates multiple features such as weather data, vegetation types, and topographical information. The research introduces a novel module for fusing multi-modal data, which enhances the performance of U-shape deep learning models like U-Net. Additionally, an innovative U-shape network structure with atrous(dilated) convolution and new attention implementation was developed to further improve prediction accuracy. The thesis also proposes an enhancement method that amplifies grouped error pixels for element-wise error computation for model training. The novel data fusion module proposed in this thesis has been proven to improve the baseline model on the F1 score, while the new model I suggest outperformed the baseline model and its two variants on the same metric. In the final part of the thesis I proposed various additional enhancement methods to improve performance further, it has shown its statistical significance under certain conditions when applied to BCELoss. By enhancing the predictive capabilities of wildfire spread models, this thesis offers valuable insights for emergency responders and policymakers, aiding in better resource allocation and risk mitigation strategies. The deep learning methodologies developed in this study are versatile and have potential applications in other fields requiring spatial data predictions, such as intelligent healthcare, flood forecasting, and disease spread modelling.
PhD Thesis

Multi-Agent Reinforcement Learning in Large Complex Environments

Sriram Ganapathi Subramanian.

UWSpace Thesis Repository, University of Waterloo, Jun, 2022.

Abs URL

Multi-agent reinforcement learning (MARL) has seen much success in the past decade. However, these methods are yet to find wide application in large-scale real world problems due to two important reasons. First, MARL algorithms have poor sample efficiency, where many data samples need to be obtained through interactions with the environment to learn meaningful policies, even in small environments. Second, MARL algorithms are not scalable to environments with many agents since, typically, these algorithms are exponential in the number of agents in the environment. This dissertation aims to address both of these challenges with the goal of making MARL applicable to a variety of real world environments. Towards improving sample efficiency, an important observation is that many real world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. A useful possibility that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this dissertation, we provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. To this end, we propose a general model for learning from external advisors in MARL and show that desirable theoretical properties such as convergence to a unique solution concept, and reasonable finite sample complexity bounds exist, under a set of common assumptions. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors. Towards scaling MARL, we explore the use of mean field theory. Mean field theory provides an effective way of scaling multi-agent reinforcement learning algorithms to environments with many agents, where other agents can be abstracted by a virtual mean agent. Prior work has used mean field theory in MARL, however, they suffer from several stringent assumptions such as requiring fully homogeneous agents, full observability of the environment, and centralized learning settings, that prevent their wide application in practical environments. In this dissertation, we extend mean field methods to environments having heterogeneous agents, and partially observable settings. Further, we extend mean field methods to include decentralized approaches. We provide novel mean field based MARL algorithms that outperform previous methods on a set of large games with many agents. Theoretically, we provide bounds on the information loss experienced as a result of using the mean field and further provide fixed point guarantees for Q-learning-based algorithms in each of these environments. Subsequently, we combine our work in mean field learning and learning from advisors to show that we can achieve powerful MARL algorithms that are more suitable for real world environments as compared to prior approaches. This method uses the recently introduced attention mechanism to perform per-agent modelling of others in the locality, in addition to using the mean field for global responses. Notably, in this dissertation, we show applications in several real world multi-agent environments such as the Ising model, the ride-pool matching problem, and the massively multi-player online (MMO) game setting (which is currently a multi-billion dollar market).
MASc Thesis

Deep Representation Learning and Prediction for Forest Wildfires

Pardis Zohouri Haghian.

UWSpace Thesis Repository, University of Waterloo, May, 2019.

Abs URL

An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it.
MASc Thesis

Reinforcement Learning for Determining Spread Dynamics of Spatially Spreading Processes with Emphasis on Forest Fires

Sriram Ganapathi Subramanian.

UWSpace Thesis Repository, University of Waterloo, Apr, 2018.

Abs

Multi-agent reinforcement learning (MARL) has seen much success in the past decade. However, these methods are yet to find wide application in large-scale real world problems due to two important reasons. First, MARL algorithms have poor sample efficiency, where many data samples need to be obtained through interactions with the environment to learn meaningful policies, even in small environments. Second, MARL algorithms are not scalable to environments with many agents since, typically, these algorithms are exponential in the number of agents in the environment. This dissertation aims to address both of these challenges with the goal of making MARL applicable to a variety of real world environments. Towards improving sample efficiency, an important observation is that many real world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. A useful possibility that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this dissertation, we provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. To this end, we propose a general model for learning from external advisors in MARL and show that desirable theoretical properties such as convergence to a unique solution concept, and reasonable finite sample complexity bounds exist, under a set of common assumptions. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors. Towards scaling MARL, we explore the use of mean field theory. Mean field theory provides an effective way of scaling multi-agent reinforcement learning algorithms to environments with many agents, where other agents can be abstracted by a virtual mean agent. Prior work has used mean field theory in MARL, however, they suffer from several stringent assumptions such as requiring fully homogeneous agents, full observability of the environment, and centralized learning settings, that prevent their wide application in practical environments. In this dissertation, we extend mean field methods to environments having heterogeneous agents, and partially observable settings. Further, we extend mean field methods to include decentralized approaches. We provide novel mean field based MARL algorithms that outperform previous methods on a set of large games with many agents. Theoretically, we provide bounds on the information loss experienced as a result of using the mean field and further provide fixed point guarantees for Q-learning-based algorithms in each of these environments. Subsequently, we combine our work in mean field learning and learning from advisors to show that we can achieve powerful MARL algorithms that are more suitable for real world environments as compared to prior approaches. This method uses the recently introduced attention mechanism to perform per-agent modelling of others in the locality, in addition to using the mean field for global responses. Notably, in this dissertation, we show applications in several real world multi-agent environments such as the Ising model, the ride-pool matching problem, and the massively multi-player online (MMO) game setting (which is currently a multi-billion dollar market).

Our Papers on Forest Wildfire Management

ISPRS

A Generative Upsampling Framework for Reconstructing High-Density Tree Structures from Low-Density Airborne Lidar

Erfan Hasanpour Zaryabi, Liam Bennett, Josh Qixuan Sun, Mark Crowley, Laura Chasmer, Christopher Hopkinson, and Jeff Boisvert.

In XXV ISPRS Congress (Canadian Symposium on Remote Sensing). Toronto, Canada. Jul, 2026.

Abs

Airborne Laser Scanning (ALS) enables efficient large-scale mapping of three-dimensional forest structure due to its extensive spatial coverage, but its low point density often limits the accurate representation of forest structural attributes required for applications such as wildfire management and forest health monitoring. This study proposes a deep generative upsampling framework to enhance low- density ALS tree point clouds and generate dense representations for improved structural analysis. To improve tree reconstruction, the framework incorporates a synthetic pretraining strategy that exposes the model to complete tree geometries before fine-tuning on real data. Experimental results demonstrate that the proposed framework generates high-density tree point clouds with strong spatial similarity to ground-truth point clouds. Synthetic pretraining further improves reconstruction performance, achieving lower Chamfer Distance and higher F1-scores across multiple upsampling rates. In addition, the reconstructed point clouds increase estimation accuracy of key structural attributes, including tree height, crown area, and tree volume. Qualitative experiments on real ALS data confirms the ability of the proposed framework to recover realistic tree structures from sparse point clouds. Overall, the proposed method provides a practical approach for bridging the wide spatial coverage of ALS with the detailed structural information typically obtained from high-density lidar data.
Multi-Advisor-QL

Multi-Agent Advisor Q-Learning

Sriram Ganapathi Subramanian, Kate Larson, Matthew Taylor, and Mark Crowley

In International Joint Conference on Artificial Intelligence (IJCAI) : Journal Track. Macao, China. Aug, 2023.

Abs PDF Code URL Video

In the last decade, there have been significant advances in multi-agent reinforcement learn- ing (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possi- ble. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online sub- optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experi- ments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
Multi-Advisor-MARL

Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning

Sriram Ganapathi Subramanian, Matthew Taylor, Kate Larson, and Mark Crowley

In Proceedings of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), London, United Kingdom. Sep, 2023.

Abs PDF URL

Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from external demonstrators is a possible solution that mitigates this problem. However, most prior approaches in this area assume the presence of a single demonstrator. Leveraging multiple knowledge sources (i.e., advisors) with expertise in distinct aspects of the environment could substantially speed up learning in complex environments. This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning. The approach leverages a two-level Q-learning architecture, and extends this framework from single-agent to multi-agent settings. We provide principled algorithms that incorporate a set of advisors by both evaluating the advisors at each state and subsequently using the advisors to guide action selection. Also, we provide theoretical convergence and sample complexity guarantees. Experimentally, we validate our approach in three different test-beds and show that our algorithms give better performances than baselines, can effectively integrate the combined expertise of different advisors, and learn to ignore bad advice.
Multi-Advisor-QL

Multi-Agent Advisor Q-Learning

Sriram Ganapathi Subramanian, Kate Larson, Matthew Taylor, and Mark Crowley

Journal of Artificial Intelligence Research (JAIR). 74, May, 2022.

Abs PDF Code URL

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experi- ments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
PO-MFRL

Partially Observable Mean Field Reinforcement Learning

Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.

In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom. May, 2021.

Abs arXiv PDF

Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents. Recent research has introduced successful methods to scale multi-agent reinforcement learning algorithms to many agent scenarios using mean field theory. Previous work in this field assumes that an agent has access to exact cumulative metrics regarding the mean field behaviour of the system, which it can then use to take its actions. In this paper, we relax this assumption and maintain a distribution to model the uncertainty regarding the mean field of the system. We consider two different settings for this problem. In the first setting, only agents in a fixed neighbourhood are visible, while in the second setting, the visibility of agents is determined at random based on distances. For each of these settings, we introduce a Q-learning based algorithm that can learn effectively. We prove that this Q-learning estimate stays very close to the Nash Q-value (under a common set of assumptions) for the first setting. We also empirically show our algorithms outperform multiple baselines in three different games in the MAgents framework, which supports large environments with many agents learning simultaneously to achieve possibly distinct goals.
WildfireMLRev

A review of machine learning applications in wildfire science and management

Piyush Jain, Sean CP Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D Flannigan.

Environmental Reviews. 28, (3). Canadian Science Publishing, Jul, 2020.

Abs arXiv PDF Slides URL

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date, and then review the use of ML in wildfire science as broadly categorized into six problem domains, including: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, we identified 298 relevant publications, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high quality, and freely available wildfire data for use by practitioners of ML methods.
A Complementary Approach to Improve WildFire Prediction Systems.

Sriram Ganapathi Subramanian, and Mark Crowley

In Neural Information Processing Systems (AI for social good workshop). NeurIPS. 2018.

PDF URL
MCTS+A3C

Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings

Sriram Ganapathi Subramanian, and Mark Crowley

In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada. 2018.

Abs PDF URL

In recent years, Deep Reinforcement Learning (RL) algorithms have shown super-human performance in a variety Atari and classic board games like chess and GO. Research into applications of RL in other domains with spatial considerations like environmental planning are still in their nascent stages. In this paper, we introduce a novel combination of Monte-Carlo Tree Search (MCTS) and A3C algorithms on an online simulator of a wildfire, on a pair of forest fires in Northern Alberta (Fort McMurray and Richardson fires) and on historical Saskatchewan fires previously compared by others to a physics-based simulator. We conduct several experiments to predict fire spread for several days before and after the given spatial information of fire spread and ignition points. Our results show that the advancements in Deep RL applications in the gaming world have advantages in spatially spreading real-world problems like forest fires. \textcopyright Springer International Publishing AG, part of Springer Nature 2018.
Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images

Sriram Ganapathi Subramanian, and Mark Crowley

Frontiers in ICT. 5, (6). Frontiers, Apr, 2018.

Abs PDF URL

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.
Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning

Sriram Ganapathi Subramanian, and Mark Crowley

In Conference on Reinforcement Learning and Decision Making. Ann Arbor, MI, USA.. 2017.
Allowing a wildfire to burn: Estimating the effect on future fire suppression costs

Rachel M. Houtman, Claire A. Montgomery, Aaron R. Gagnon, David E. Calkin, Thomas G. Dietterich, Sean McGregor, and Mark Crowley

International Journal of Wildland Fire. 22, (7). 2013.

Abs PDF URL

Where a legacy of aggressive wildland fire suppression has left forests in need of fuel reduction, allowing wildland fire to burn may provide fuel treatment benefits, thereby reducing suppression costs from subsequent fires. The least-cost-plus-net-value-change model of wildland fire economics includes benefits of wildfire in a framework for evaluating suppression options. In this study, we estimated one component of that benefit – the expected present value of the reduction in suppression costs for subsequent fires arising from the fuel treatment effect of a current fire. To that end, we employed Monte Carlo methods to generate a set of scenarios for subsequent fire ignition and weather events, which are referred to as sample paths, for a study area in central Oregon. We simulated fire on the landscape over a 100-year time horizon using existing models of fire behaviour, vegetation and fuels development, and suppression effectiveness, and we estimated suppression costs using an existing suppression cost model. Our estimates suggest that the potential cost savings may be substantial. Further research is needed to estimate the full least-cost-plus-net-value-change model. This line of research will extend the set of tools available for developing wildfire management plans for forested landscapes.