As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, responding to and mitigating the damages caused by these fires. Machine learning techniques provide a potential approach for developing such models. The objective of this study is to evaluate the feasibility of using a Convolutional Long Short-Term Memory (ConvLSTM) recurrent neural network to model the dynamics of wildland fire propagation. The machine learning model is trained on simulated wildfire data generated by a mathematical analogue model. Three simulated datasets are analyzed, each with increasing degrees of complexity. The simplest dataset includes a constant wind direction as a single confounding factor, whereas the most complex dataset includes dynamic wind, complex terrain, spatially varying moisture content and heterogenous vegetation density distributions. We examine how effective the ConvLSTM can learn the fire-spread dynamics over consecutive time steps. It is shown that ConvLSTMs can capture local fire transmission events, as well as the overall fire dynamics, such as the rate at which the fire spreads. Finally, we demonstrate that ConvLSTMs outperform other network architectures that have previously been used to model similar wildland fire dynamics.
Université de Moncton（蒙克顿大学）；Universidad Técnica Federico Santa María（费德里科圣玛丽亚技术大学）
Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground…
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Wildfire has had increasing impacts on society as the climate changes and the wildland urban interface grows. As such, there is a demand for innovative solutions to help manage fire. Managing wildfire can include proactive fire management such as prescribed burning within constrained areas or advancements for reactive fire management (e.g., fire suppression). Because of the growing societal impact, the JPL BlueSky program sought to assess the current state of fire management and technology and determine areas with high return on investment. To accomplish this, we met with the national interage…
Modelling wildland fire burn severity in California using a spatial Super Learner approach
University of Minnesota - Twin Cities（明尼苏达大学双城分校）；North Carolina State University（北卡罗来纳州立大学）；University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Vassar College（瓦萨学院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Mechanical and Aerospace Engineering, AIAA Student Member（机械与航空航天工程，AIAA学生会员）；Department of Computer Science（计算机科学系）；School of Environment and Natural Resources（环境与自然资源学院）；Computer Science and Engineering（计算机科学与工程）；Department of Electronics and Telecommunications Engineering（电子与电信工程系）；Aeronautical and Astronautical Engineering, AIAA Associate Fellow（航空与航天工程，AIAA associate fellow）
GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
Urban Reslience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University（Urban Reslience.AI实验室，Zachry土木与环境工程系，德克萨斯A&M大学）；Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）
Metropolitan State University of Denver（丹佛大都会州立大学）
Wildland fires pose an increasingly serious problem in our society. The number and severity of these fires has been rising for many years. Wildfires pose direct threats to life and property as well as threats through ancillary effects like reduced air quality. The aim of this thesis is to develop techniques to help combat the impacts of wildfires by improving wildfire modeling capabilities by using satellite fire observations. Already much work has been done in this direction by other researchers. Our work seeks to expand the body of knowledge using mathematically sound methods to utilize info…
Lynxi Technologies Co., Ltd.（灵汐科技有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-plausible neuronal dynamics and simplicity, LIF-SNN benefits from event-driven processing, however, usually faces the embarrassment of reduced performance. This may because in LIF-SNN the neurons transmit information via spikes. To address this issue, in this work, we propose a Leaky Integrate and Analog Fire (LIAF) neuron model, so that analog values can be transmitted among neurons, and a deep network termed as LIAF…
In this work, we explore a new Spiking Neural Network (SNN) formulation with Resonate-and-Fire (RAF) neurons (Izhikevich, 2001) trained with gradient descent via back-propagation. The RAF-SNN, while more biologically plausible, achieves performance comparable to or higher than conventional models in the Machine Learning literature across different network configurations, using similar or fewer parameters. Strikingly, the RAF-SNN proves robust against noise induced at testing/training time, under both static and dynamic conditions. Against CNN on MNIST, we show 25% higher absolute accuracy with…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局）
Poor air quality can have a significant impact on human health. The National Oceanic and Atmospheric Administration (NOAA) air quality forecasting guidance is challenged by the increasing presence of extreme air quality events due to extreme weather events such as wild fires and heatwaves. These extreme air quality events further affect human health. Traditional methods used to correct model bias make assumptions about linearity and the underlying distribution. Extreme air quality events tend to occur without a strong signal leading up to the event and this behavior tends to cause existing met…
Massachusetts Institute of Technology（麻省理工学院）；University of California, Berkeley（加州大学伯克利分校）
Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the burning of solid fuels remains poorly understood, which can be partly attributed to the unknown chemical kinetics of most solid fuels. Most available kinetic models were built upon expert knowledge, which requires chemical insights and years of experience. This work presents a framework for autonomously discovering biomass pyrolysis kinetic models from thermogravimetric analyzer (TGA) experimental data using the rec…
Wildfire modelling is an attempt to reproduce fire behaviour. Through active fire analysis, it is possible to reproduce a dynamical process, such as wildfires, with limited duration time series data. Recurrent neural networks (RNNs) can model dynamic temporal behaviour due to their ability to remember their internal input. In this paper, we compare the Gated Recurrent Unit (GRU) and the Long Short-Term Memory (LSTM) network. We try to determine whether a wildfire continues to burn and given that it does, we aim to predict which one of the 8 cardinal directions the wildfire will spread in. Over…
QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
eBrain Lab, Division of Engineering, New York University Abu Dhabi（eBrain实验室，工程系，纽约大学阿布扎克分校）；Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi（量子与拓扑系统中心，NYUAD研究学院，纽约大学阿布扎克分校）
Stanford University School of Medicine（斯坦福大学医学院）；The Harker School（哈克学校）
Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data fro…
