A review of machine learning applications in wildfire science and management

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.Comment: 83 pages, 4 figures, 3 table

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

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 introduce a novel approach for learning in SSP domains such as wild fires 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 into any point in the 3 $\times$ 3 grid around it (including 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 use 3 demonstrative domains to prove the ability of our approach. The first one is a popular online simulator of a wildfire, the second domain involves a pair of forest fires in Northern Alberta which are the Fort McMurray fire of 2016 that led to an unprecedented evacuation of almost 90,000 people and the Richardson fire of 2011, and the third domain deals with historical Saskatchewan fires previously compared by others to a physics-based simulator. The standard RL algorithms considered on all the domains include Monte Carlo Tree Search, Asynchronous Advantage Actor-Critic (A3C), Deep Q Learning (DQN) and Deep Q Learning with prioritized experience replay. We also introduce a novel combination of Monte-Carlo Tree Search (MCTS) and A3C algorithms that shows the best performance across different test domains and testing environments. Additionally, some other algorithms like Value Iteration, Policy Iteration and Q-Learning are applied on the Alberta fires testing domain to show the performances of these simple model based and model free approaches. We also compare to a Gaussian process based supervised learning approach and discuss relation to state-of-the-art methods from forest wildfire modelling. The results show that we can learn predictive, agent-based policies as models of spatial dynamics using RL on readily available datasets like satellite images which are at least as good as other methods and have many additional advantages in terms of generalizability and interpretability

Some results on a set of data driven stochastic wildfire models

Across the globe, the frequency and size of wildfire events are increasing. Research focused on minimizing wildfire is critically needed to mitigate impending humanitarian and environmental crises. Real-time wildfire response is dependent on timely and accurate prediction of dynamic wildfire fronts. Current models used to inform decisions made by the U.S. Forest Service, such as Farsite, FlamMap and Behave do not incorporate modern remotely sensed wildfire records and are typically deterministic, making uncertainty calculations difficult. In this research, we tested two methods that combine artificial intelligence with remote sensing data. First, a stochastic cellular automata that learns algebraic expressions was fit to the spread of synthetic wildfire through symbolic regression. The validity of the genetic program was tested against synthetic spreading behavior driven by a balanced logistic model. We also tested a deep learning approach to wildfire fire perimeter prediction. Trained on a time-series of geolocated fire perimeters, atmospheric conditions, and satellite images, a deep convolutional neural network forecasts the evolution of the fire front in 24-hour intervals. The approach yielded several relevant high-level abstractions of input data such as NDVI vegetation indexes and produced promising initial results. These novel data-driven methods leveraged abundant and accessible remote sensing data, which are largely unused in industry level wildfire modeling. This work represents a step forward in wildfire modeling through a curated aggregation of satellite image spectral layers, historic wildfire perimeter maps, LiDAR, atmospheric conditions, and two novel simulation models. The results can be used to train and validate future wildfire models, and offer viable alternatives to current benchmark physics-based models used in industry

Improving the estimation of fire danger, fire propagation and fire monitoring : new insights using remote sensing data and statistical methods

This thesis covers three major topics related to wildfires, remote sensing and meteorology: (i) quantifying and forecasting fire danger combining numerical weather forecasts and satellite observations of fire intensity; (ii) mapping burned areas from satellite observations with multiple spatial and spectral resolution; and (iii) modelling fire progression taking into account weather conditions and fuel (vegetation) availability. Regarding the first topic, an enhanced Fire Weather Index (FWI) is proposed by using statistical methods to combine the classical FWI with an atmospheric instability index with the aim of better forecasting the fire danger conditions favourable to the development of convective fires. Furthermore, the daily definition of the classical FWI was extended to an hourly timescale, allowing for assessment of the variability of the fire danger conditions throughout the day. For the second topic, a method is proposed to map and date burned areas using sequences of daily satellite data. This method, tested over several regions around the globe, provide burned area maps that outperform other existing methods for the task, particularly regarding the consistency and accuracy of the date of burning. Furthermore, a method is proposed for fast assessment of burned areas using 10-meter resolution satellite data and making use of Google Earth Engine (GEE) as a tool for preprocessing and downloading of data that is then used as input to a deep learning model that combines a coarse burned area map with the medium resolution data to provide a refined burned area map with 10-meter resolution at event level and with low computational requirements. Finally, for the third topic, a method is proposed to estimate the fire progression over a 12-hour period with resource to an ensemble of models trained based on the reconstruction of past events. Overall, I am confident that the results obtained and presented in this thesis provide a significant contribution to the remote sensing and wildfires scientific community while opening interesting paths for future research on the topics described

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Empirical investigation of global wildfire drivers and development of a new flammability parametrisation for the INFERNO fire model

Wildfires have a significant impact on the Earth’s vegetation, atmospheric composition, and climate. There is also growing evidence that fire behaviour has already been altered in response to climate change. Given the anticipated increase in climate conditions conducive to wildfires in many regions of the world, there is an urgent need to advance our understanding of the drivers of wildfires and improve their representation in global Earth system models. Recognising and predicting such responses to climate change and the associated feedbacks is one of the key challenges of the field, and integrated vegetation–fire models have the potential to accomplish this goal. However, the relationship between wildfire activity and past vegetation productivity is still unclear. It has been hypothesised that this strongly contributes to the poor performance of state-of-the-art fire models in representing observed vegetation–fire relationships. Thus, this thesis examines the role of seasonal and long-term vegetation dynamics (and fuel accumulation dynamics) that are of fundamental importance for global wildfires due to their impact on fuel availability during the fire season. In addition to instantaneous climatic conditions, the seasonality of antecedent vegetation and climate conditions controlling fuel build-up and fuel drying was found to be important for the prediction of burnt area in an empirical analysis of wildfire drivers. These were then used to modify the vegetation parametrisation of the INFERNO fire model. By using sigmoidal relationships to explicitly consider antecedent vegetation and climate conditions, global performance was improved. Finally, the new model was evaluated using sensitivity analyses, demonstrating the overarching importance of dryness and temperature while also disentangling the different impacts model parameters have on the magnitude and phase performance of the new parametrisation.Open Acces

Envisioning the Future Role of 3D Wireless Networks in Preventing and Managing Disasters and Emergency Situations

In an era marked by unprecedented climatic upheavals and evolving urban landscapes, the role of advanced communication networks in disaster prevention and management is becoming increasingly critical. This paper explores the transformative potential of 3D wireless networks, an innovative amalgamation of terrestrial, aerial, and satellite technologies, in enhancing disaster response mechanisms. We delve into a myriad of use cases, ranging from large facility evacuations to wildfire management, underscoring the versatility of these networks in ensuring timely communication, real-time situational awareness, and efficient resource allocation during crises. We also present an overview of cutting-edge prototypes, highlighting the practical feasibility and operational efficacy of 3D wireless networks in real-world scenarios. Simultaneously, we acknowledge the challenges posed by aspects such as cybersecurity, cross-border coordination, and physical layer technological hurdles, and propose future directions for research and development in this domain

Landscape - wildfire interactions in southern Europe: implications for landscape management

ReviewEvery year approximately half a million hectares of land are burned by wildfires in southern Europe, causing large ecological and socio-economic impacts. Climate and land use changes in the last decades have increased fire risk and danger. In this paper we review the available scientific knowledge on the relationships between landscape and wildfires in the Mediterranean region, with a focus on its application for defining landscape management guidelines and policies that could be adopted in order to promote landscapes with lower fire hazard. The main findings are that (1) socio-economic drivers have favoured land cover changes contributing to increasing fire hazard in the last decades, (2) large wildfires are becoming more frequent, (3) increased fire frequency is promoting homogeneous landscapes covered by fire-prone shrublands; (4) landscape planning to reduce fuel loads may be successful only if fire weather conditions are not extreme. The challenges to address these problems and the policy and landscape management responses that should be adopted are discussed, along with major knowledge gapsinfo:eu-repo/semantics/publishedVersio