A review of machine learning applications in wildfire science and management

Abstract

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) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 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. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, 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 requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

83 pages, 4 figures, 3 tables

References in corpus (8)

• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Flood Prediction Using Machine Learning Models: Literature Review
• A trans-disciplinary review of deep learning research for water resources scientists
• Neural networks for post-processing ensemble weather forecasts
• A review of wildland fire spread modelling, 1990-present, 1: Physical and quasi-physical models
• A review of wildland fire spread modelling, 1990-present 2: Empirical and quasi-empirical models
• A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models
• Wildfire Prediction to Inform Fire Management: Statistical Science Challenges

Cited by in corpus (17)

• Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
• Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy
• Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting
• A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes
• FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
• A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models
• Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
• PyTorchFire: A GPU-Accelerated Wildfire Simulator with Differentiable Cellular Automata
• Modelling wildland fire burn severity in California using a spatial Super Learner approach
• Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
• Data Science for Geographic Information Systems
• A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
• A Neural Emulator for Uncertainty Estimation of Fire Propagation
• GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
• CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
• Automating the Wildfire Detection and Scheduling Pipeline with Maneuverable Earth Observation Satellites
• Neural-Parameterized Cellular Automata for Wildfire Spread