Prediction

See also: Deep Neural Networks, Reinforcement Learning, Forest Fires, Prediction, Classification, Forest Fire Prediction and Control, Highlighted Papers
Abstract
mltree.pdf
papers2.pdf
papers1.pdf
Final Published Version
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 identfied 300 relevant publications up to the end of 2019, 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 methods 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.
Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks, at European Conference on Machine Learning (ECML 2019), Wurzburg, Germany, Thursday, September 19, 2019:
ecml2019_talk.pdf

The computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it intractable to compute scale-resolving simulations. For most engineering applications, the large scale separation between the flame (typically sub-millimeter scale) and the characteristic turbulent flow (typically centimeter or meter scale) allows us to evoke simplifying assumptions–such as done for the flamelet model–to pre-compute all the chemical reactions and map them to a low-order manifold. The resulting manifold is then tabulated...

Read more about Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks
Bhalla, S. et al., 2019. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks. In European Conference on Machine Learning. Wurzburg, Germany, p. 8.
See also: Deep Neural Networks, Manifold Learning, Physics and Chemistry, Prediction, Combustion Modelling, Data Reduction
Abstract
ecml_combustion_ml.pdf

Example of Flamelet modelThe computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it intractable to compute scale-resolving simulations. For most engineering applications, the large scale separation between the flame (typically sub-millimeter scale) and the characteristic turbulent flow (typically centimeter or meter scale)  allows us to evoke simplifying assumptions--such as done for the flamelet model--to pre-compute all the chemical reactions and map them to a low-order manifold. The resulting manifold is then tabulated and looked-up at run-time. As the physical complexity of combustion simulations increases (including radiation, soot formation, pressure variations etc.) the dimensionality of the resulting manifold grows which impedes an efficient tabulation and look-up. In this paper we present a novel approach to model the multi-dimensional combustion manifold. We approximate the combustion manifold using a neural network function approximator and use it to predict the temperature and composition of the reaction. We present a novel training procedure which is developed to generate a smooth output curve for temperature over the course of a reaction. We then evaluate our work against the current approach of tabulation with linear interpolation in combustion simulations. We also provide an ablation study of our training procedure in the context of over-fitting in our model. The combustion dataset used for the modeling of combustion of H2 and O2 in this work is
released alongside this paper. See the poster version here.

 

Prediction Results

Paper accepted to ECML 2019

June 13, 2019
We recently had a paper accepted to the 2019 European Conference on Machine Learing (ECML) on the topic of modelling of combustion processes using Deep Neural Networks. This is an exciting topic that could lead to huge improvements in spead of design and testing for combustion engines. We'll post the camera ready once it's updated. Looking forward to presnting this in Wurzburg, Germany in September! Read more about Paper accepted to ECML 2019
See also: Deep Neural Networks, Physics and Chemistry, Prediction
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:
BIRS2017.pdf
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
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...

Read more about Using Deep Learning and Reinforcement Learning to Tame Spatially Spreading Processes
BIRC Workshop On Deep Learning In Medicine, at University Hospital, London, Ontario, Canada, Monday, August 28, 2017:
deeplearningmedprez_-_final.pdf

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...

Read more about BIRC Workshop On Deep Learning In Medicine