Forecasting Post-Wildfire Vegetation Recovery in California using a Convolutional Long Short-Term Memory Tensor Regression Network
Western Connecticut State University（西康涅狄格州立大学）
Jiahe Liu, Xiaodi Wang
备注：
To be included in the 6th Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at the 37th Conference on Neural Information Processing Systems
The study of post-wildfire plant regrowth is essential for developing successful ecosystem recovery strategies. Prior research mainly examines key ecological and biogeographical factors influencing post-fire succession. This research proposes a novel approach for predicting and analyzing post-fire plant recovery. We develop a Convolutional Long Short-Term Memory Tensor Regression (ConvLSTMTR) network that predicts future Normalized Difference Vegetation Index (NDVI) based on short-term plant growth data after fire containment. The model is trained and tested on 104 major California wildfires occurring between 2013 and 2020, each with burn areas exceeding 3000 acres. The integration of ConvLSTM with tensor regression enables the calculation of an overall logistic growth rate k using predicted NDVI. Overall, our k-value predictions demonstrate impressive performance, with 50% of predictions exhibiting an absolute error of 0.12 or less, and 75% having an error of 0.24 or less. Finally, we employ Uniform Manifold Approximation and Projection (UMAP) and KNN clustering to identify recovery trends, offering insights into regions with varying rates of recovery. This study pioneers the combined use of tensor regression and ConvLSTM, and introduces the application of UMAP for clustering similar wildfires. This advances predictive ecological modeling and could inform future post-fire vegetation management strategies.
This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015 to 2019 in Pacific Gas & Electricity territory along with various weather, vegetation, and very high resolution data on grid infrastructure including location, age, materials. With these data we explore a range of machine learning methods and strategies to manage training data imbalance. The best area under the receiver operating characteristic we obtain …
Stanford University（斯坦福大学）；Department of Energy Resources Engineering（能源资源工程系）
Electric utilities are struggling to manage increasing wildfire risk in a hotter and drier climate. Utility transmission and distribution lines regularly ignite destructive fires when they make contact with surrounding vegetation. Trimming vegetation to maintain the separation from utility assets is as critical to safety as it is difficult. Each utility has tens of thousands of linear miles to manage, poor knowledge of where those assets are located, and no way to prioritize trimming. Feature-enhanced convolutional neural networks (CNNs) have proven effective in this problem space. Histograms …
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）
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus o…
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）
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…
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topo…
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ign…
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…
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）
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）
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）
Set Prediction for Next-Day Active Fire Forecasting
Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Orion Lab（奥里昂实验室）；University of Utah（犹他大学）；National Observatory of Athens（雅典国家天文台）；Earth System Science Program, School of Sciences and Engineering, Universidad del Rosario（地球系统科学计划，科学与工程学院，罗萨里奥大学）；Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova Lisboa（环境科学与工程系，科学与技术学院，新里斯本大学）；ELLIS Unit Jena（耶纳ELLIS单位）
FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Desert Research Institute（沙漠研究所）；Amazon（亚马逊公司）；United States Forest Service（美国林务局）
本文提出 FUELVISION 算法，融合多源遥感与地形数据，结合生成式 AI 合成数据增强训练集，采用多模型集成方法实现近实时野火可燃物制图，准确率近 80%。
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…
