Computational Sustainability

One major guiding inspiration for some of the theoretical directions in the lab are problems from the growing field of **Computational Sustainability** which applies AI/ML methods to data analysis and optimization problems in sustainability related domains. These domains include ecology, agriculture, water systems, energy systems and climate change.
 

Adaptation Through Learning: Using Machine Learning to Improve Forest Wildfire Management

June 17, 2020
https://www.re-work.co/events/webinar-ai-for-crisis-prediction-2020/schedule
 

In this talk I will provide a window into this situation by looking at forest wildfire management as a case study of a rich domain where some work has been done but huge opportunities remain. Existing forest wildfire spread models are complex manual constructions that are struggling to adapt to changing climate as well as changing attitudes towards forests.

Deep Learning learning algorithms are being...

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Fighting Fire with AI: Using Artificial Intelligence to Improve Modelling and Decision Making in Wildfire Management, at Banff International Research Station, Banff, Alberta, Canada, Friday, November 17, 2017:
I was invited to speak at this week-long workshop at the fabulous BIRS facility in Banff Alberta. The workshop was entitled "Forest and Wildland Fire Management: a Risk Management Perspective" which brought together a wide range of experts and stakeholders from across Canada as well as some researchers from around the world to discuss the latest research on Forest Fire Management. It was an incredibly productive week that built many new connections. Read more about Fighting Fire with AI: Using Artificial Intelligence to Improve Modelling and Decision Making in Wildfire Management
Results of fire spread prediction from different ML algorithms
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.
Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings
Subramanian, S.G. & Crowley, M., 2018. Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings. In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada: Springer, pp. 285-291. Available at: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_28. Publisher's Version

 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. 

Using Deep Learning and Reinforcement Learning to Tame Spatially Spreading Processes, at University of Waterloo, Wednesday, October 25, 2017

This was an invited talk for the Waterloo Institute for Complexity and Innovation (WICI) seminar series. The talk was recorded and can be watched from WICI's website here.

Abstract:

Recent advances in Artificial Intelligence and Machine Learning (AI/ML) allow us to learn predictive models and control policies for larger, more complex systems than ever before. However, some important real world domains such as...

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BIRC Workshop On Deep Learning In Medicine, at University Hospital, London, Ontario, Canada, Monday, August 28, 2017:

This all-day workshop brough together researchers, students and medical professionals from medical imaging, image processing and machine learning to discuss what the new class of machine learning algorithms known collectively as Deep Learning are, how they are and could be used for medicine and what the impacts for medicine as a whole are of this technology. The workshop was hosted by the Biomedical Imaging Research Centre (BIRC) at the University of Western Ontario. I gave an introductory...

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Houtman, R.M. et al., 2013. Allowing a wildfire to burn: Estimating the effect on future fire suppression costs. International Journal of Wildland Fire, 22(7), pp.871–882.
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.