Forest Wildfire Management

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

The task of Forest Fire Management 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:

  • 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.

Relevant work to this project has been progressing recently on theoretical models of multi-agent decision making. This research explores the intersection of theory in the field of Game Theory and Multi-Agent Reinforcement Learning (MARL), both of which study how multiple agents can adapt and optimize their decision making policies in response to changes in the world around them and actions of other decision makers.


  • ML for Forest Fire Journal Paper: In 2020, in collaboration with some other investigators in this strategic network, we published a review article in the Environmental Reviews journal \cite{jain2020review} analysing the relevance of various ML algorithms to the domain and exhaustively surveying and analysing the existing research using ML for forest fire management.
  • NSERC/CANADA WILDFIRE STRATEGIC NETWORK: In 2019, an NSERC Strategic Network grant was confirmed in which we are involved to support computational research in to Forest Fire Management practices. This includes the AI/ML/RL focus of our research along with our colleague in computer science Prof. Kate Larson.
  • In 2019, we were awarded a computing grant on the topic of Wildfire management: Disaster response, climate change” from Waterloo Artificial Intelligence Institute (WAII) in affiliation with the Microsoft “AI for Social Good” program.

Our Papers on Forest Fire Management

  1. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    Frontiers in Artificial Intelligence. sep, 2022.
  2. MARLEmpircal
    Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
    In NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
  3. Multi-Advisor-QL
    Multi-Agent Advisor Q-Learning
    Journal of Artificial Intelligence Research (JAIR). 73, may, 2022.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.