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. Overall the GRU performs better for longer time series than the LSTM. We have shown that although we are reasonable at predicting the direction in which the wildfire will spread, we are not able to asses if the wildfire continues to burn due to the lack of auxiliary data.
The current projection shows that much of the continental U.S. will have significantly hotter and drier days in the following decades, leading to more wildfire hazards that threaten the safety of power grid. Unfortunately, the U.S. power industry is not well prepared and still predominantly relies on empirical fire indices which do not consider the full spectrum of dynamic environmental factors. This paper proposes a new spatio-temporal point process model, Convolutional Non-homogeneous Poisson Process (cNHPP), to quantify wildfire risks for power delivery networks. The proposed model captures…
Wildfires can be devastating, causing significant damage to property, ecosystem disruption, and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-time wildfire management. To this end, substantial attention in the wildifre literature has focused on the level set method, which effectively represents complicated boundaries and their change over time. Nevertheless, most of these approaches rely on a heavily-parameterized formulas for spread and fail to account for the uncertainty in the forecast. The rapid evolution of large wildfires and inhomogeneous environmen…
Fire has been an integral part of the Earth for millennia. Several recent wildfires have exhibited an unprecedented spatial and temporal extent and their control is beyond national firefighting capabilities. Prescribed or controlled burning treatments are debated as a potential measure for ameliorating the spread and intensity of wildfires. Machine learning analysis using random forests was performed in a spatio-temporal data set comprising a large number of savanna fires across 22 years. Results indicate that fire return interval was not an important predictor of fire spread rate or fire inte…
Google Research（谷歌研究院）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques offer the ability to bridge these objectives by learning first-principles physics while achieving computa…
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris（CEREA、ENPC、EDF R&D、巴黎理工学院）；Department of Earth Science & Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；Department of Computing, Imperial College London（帝国理工学院伦敦计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院伦敦数据科学研究院）
Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter
H. Milton Stewart School of Industrial and Systems Engineering（H. Milton Stewart工业与系统工程学院）；School of Earth and Atmospheric Sciences（地球与大气科学学院）；Georgia Institute of Technology（佐治亚理工学院）
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
University of Southern California（南加州大学）；University of Maryland（马里兰大学）；San Jose State University（圣何塞州立大学）；University of Colorado Denver（科罗拉多大学丹佛分校）；Colorado State University（科罗拉多州立大学）；University of Utah（犹他大学）
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Faculty of Science, University of Alberta（阿尔伯塔大学理学院）；Interdisciplinary Lab for Mathematical Ecology & Epidemiology (ILMEE), University of Alberta（阿尔伯塔大学数学生态与流行病学跨学科实验室）；Department of Mathematical and Statistical Sciences, University of Alberta（阿尔伯塔大学数学与统计科学系）
Intense wildfires impact nature, humans, and society, causing catastrophic damage to property and the ecosystem, as well as the loss of life. Forecasting wildfire front propagation is essential in order to support fire fighting efforts and plan evacuations. The level set method has been widely used to analyze the change in surfaces, shapes, and boundaries. In particular, a signed distance function used in level set methods can readily be interpreted to represent complicated boundaries and their changes in time. While there is substantial literature on the level set method in wildfire applicati…
The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models t…
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction. The main programming language used is Julia, a compiled language which offers better perfomance than interpreted ones, providing Just in Time (JIT) compilation with different optimization levels. Moreover…
California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence
Stanford University（斯坦福大学）；Harvard University T. H. Chan School of Public Health（哈佛大学T.H. Chan公共卫生学院）；University of Texas Health Science Center（德克萨斯大学健康科学中心）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Bayesian Optimization algorithm has become a promising approach for nonlinear global optimization problems and many machine learning applications. Over the past few years, improvements and enhancements have been brought forward and they have shown some promising results in solving the complex dynamic problems, systems of ordinary differential equations where the objective functions are computationally expensive to evaluate. Besides, the straightforward implementation of the Bayesian Optimization algorithm performs well merely for optimization problems with 10-20 dimensions. The study presented…
Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective suppression response. Current wildfire detection models are not versatile enough for the low-technology conditions of South American hot spots. This deep learning study first trains a Fully Convolutional Neural Network on Landsat 8 images of Ecuador and the Galapagos, using Green and Short-wave Infrared bands to predict pixel-level binary fire masks. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on…
Generative Algorithms for Wildfire Progression Reconstruction from Multi-Modal Satellite Active Fire Measurements and Terrain Height
Department of Aerospace and Mechanical Engineering, University of Southern California（航空航天与机械工程系，美国南加州大学）；Department of Mathematics, University of Southern California（数学系，美国南加州大学）；Cooperative Institute for Research in the Atmosphere, Colorado State University（大气研究联合机构，科罗拉多州立大学）；Department of Meteorology and Climate Science, San Jose State University（气象与气候科学系，圣何塞州立大学）；Department of Atmospheric Sciences, University of Utah（大气科学系，犹他大学）；Department of Mathematical and Statistical Sciences, University of Colorado Denver（数学与统计科学系，科罗拉多大学丹佛分校）
In order to capture the dependence in the upper tail of a time series, we develop non-negative regularly-varying time series models that are constructed similarly to classical non-extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly-varying time series through the tail pairwise dependence function (TPDF) which is a measure of pairwise extremal dependencies. We state consistency requirements among the finite-dimensional collections of the elements of a regularly-varying t…
Detecting the spread of coronavirus will go a long way toward reducing human and economic loss. Unfortunately, existing Epidemiological models used for COVID 19 prediction models are too slow and fail to capture the COVID-19 development in detail. This research uses Partial Differential Equations to improve the processing speed and accuracy of forecasting of COVID 19 governed by SEIRD model equations. The dynamics of COVID 19 were extracted using Convolutional Neural Networks and Deep Residual Recurrent Neural Networks from data simulated using PDEs. The DRRNNs accuracy is measured using Mean …
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day's fire danger. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. We implement a variety of Deep Learning (DL) model…
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
College of Engineering, University of Georgia（佐治亚大学工程学院）；Information System Department, UMBC（马里兰大学巴尔的摩郡分校信息系统系）；College of Graduate and Professional Studies, Trine University（特瑞大学研究生与专业研究学院）；School of Professional Studies, Columbia University（哥伦比亚大学专业研究学院）
University of Cambridge（剑桥大学）；University of Valencia（瓦伦西亚大学）；Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；Columbia University（哥伦比亚大学）；University of Oxford（牛津大学）；Pasteur Labs（巴斯德实验室）；ISI
Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable, and therefore dangerous, wildfire spread. They can also inject smoke particles and trace gases into the upper troposphere and lower stratosphere, affecting the Earth's climate. As global temperatures increase, these previously rare events are becoming more common. Being able to predict which fires are likely to generate pyroCb is therefore key to climate adaptation in wildfire-prone areas. This paper introduces Pyrocast, a pipeline for pyroCb analysis and forecasting. Th…
University of Chile（智利大学）；University of California Berkeley（加州大学伯克利分校）
In practical applications, it is common that wildfire simulators do not correctly predict the evolution of the fire scar. Usually, this is caused due to multiple factors including inaccuracy in the input data such as land cover classification, moisture, improperly represented local winds, cumulative errors in the fire growth simulation model, high level of discontinuity/heterogeneity within the landscape, among many others. Therefore in practice, it is necessary to adjust the propagation of the fire to obtain better results, either to support suppression activities or to improve the performanc…
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 m…
University of Southern California（南加州大学）；Google（谷歌）；Google Research（谷歌研究院）
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content. This work presents techniques for classifying linked content spread on forum websites -- specifically, links to news articles or blogs -- using user interaction signals alone. Importantly, online forums such as Reddit do not have a user-generated social graph, which is assumed in social network behavioral-based classification settings. Using Reddit as a case-study, we show how to obtain a derived social graph, and use this graph, Reddit post sequences,…
Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people a…
Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent i…
Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder decoder based machine learning mod…
