Wildfire Prediction

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction
Oct 5, 2026Zhisheng Qi, Li Zhu, Utkarsh Sahu +3Wildfire Prediction
PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States
Sep 28, 2026Arya Kondur, Giosue Migliorini, Cameron Schmitt +14Wildfire Prediction Wildfire
Department of Computer Science, University of California, Irvine, CA 92697, USA · Department of Statistics, University of California, Irvine, CA 92697, USA · Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA +6
When local gains fail to transfer: Frozen Earth-observation embeddings across wildfires
Sep 28, 2026Philipp Stark, Alexandros Sopasakis, Ola HallWildfire Prediction Wildfire
Department of Human Geography Lund University Sölvegatan 10, SE-223 62 Lund, Sweden · Centre for Mathematical Sciences Lund University Sölvegatan 18, SE-221 00 Lund, Sweden
Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations
Sep 16, 2026Marcin Lawenda, Aleksandra Krasicka, David Caballero +2Wildfire Prediction Accurate Flood Prediction
Poznan Supercomputing and Networking Center, Poznań, Poland · MeteoGrid, Madrid, Spain · Technical University of Czestochowa, Częstochowa, Poland
Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction
Sep 15, 2026Miguel Esparza, Aydin Ayanzadeh Ahmad Mousavi, Ali MostafaviWildfire Prediction Accurate Flood Prediction
Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA · Department of Mathematics and Statistics, American University, Washington, D.C. 20016, USA
Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
Sep 1, 2026Matheus F. Kovaleski, Cristiano Premebida, João Ruivo PauloWildfire Detection Wildfire Prediction
Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Portugal
Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling
Sep 1, 2026Siyu Chen, Esha Saha, Hao WangWildfire Prediction Random Forest
Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping
Aug 12, 2026Yuan Zhuang, Sanaa Hobeichi, Peng Shi +1Wildfire Prediction Geospatial Foundation Models
1UNSW Sydney, School of Risk and Actuarial Studies, New South Wales, Australia · 2ARC Centre of Excellence for the Weather of the 21st Century, Australia · 3Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia +2
Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction
Aug 5, 2026Quinn Ledingham, Zhengsen Xu, Yimin Zhu +6Wildfire Prediction Rainfall
Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
Real-time physics inversion for retrieval of sub-pixel wildfire temperatures from VSWIR imaging spectroscopy
Aug 4, 2026William R. Keely, Philip G. Brodrick, Katherine Mistick +3Wildfire Prediction Rgb-Thermal
Jet Propulsion Laboratory, California Institute of Technology, CA, United States of America · School of Environment, Society & Sustainability, University of Utah, Salt Lake City, UT, United States of America
Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations
Jul 21, 2026Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez +12Wildfire Prediction Tensor Networks
1Dipartimento Interuniversitario di Fisica, Universit`a degli Studi di Bari Aldo Moro, Bari, I-70126, Italy · 2Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, I-70126, Italy · 3Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Universit`a degli Studi di Bari Aldo Moro, Bari, I-70124, Italy +2
LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
Jul 9, 2026Xiao Fu, Yue Hu, Meida Chen +2Digital Elevation Models Wildfire Prediction
University of Southern California
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
Jul 8, 2026Yongcan Huang, Li Jiang, Ze Yu LiuWildfire Prediction Time Series Foundation Models
College of Engineering, University of Georgia, Athens, Georgia, USA · Information System Department, UMBC, Baltimore, Maryland, USA · College of Graduate and Professional Studies, Trine University, Angola, Indiana, USA +1
Physics-guided spatiotemporal neural models for fuel density prediction
Jul 8, 2026Tolga Caglar, Jaynil Jaiswal, Saqib Azim +3Physics-Guided Machine Learning Wildfire Prediction
San Diego Supercomputer Center University of California, San Diego
Neural Tree Reconstruction for the Open Forest Observatory
Jun 16, 2026Marissa Ramirez de Chanlatte, Arjun Rewari, Trevor Darrell +1Forest Biomass Trees
Berkeley AI Research · University of California, Berkeley · Department of Plant Sciences University of California, Davis
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Jun 15, 2026Maulik Srivastava, Esha Saha, Hao WangWildfire Prediction Satellite Imagery
Faculty of Science, University of Alberta · Interdisciplinary Lab for Mathematical Ecology & Epidemiology (ILMEE), University of Alberta · Department of Mathematical and Statistical Sciences, University of Alberta
ShearFuse-UNet: Hadamard, DCT, and Shearlet Transform Fusion for Next-Day Wildfire Spread Prediction
Jun 12, 2026Ene Meco, Yingyi Luo, Emadeldeen Hamdan +2Wildfire Prediction U-Net
Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA · US Forest Service, Pacific Wildland Fire Science Laboratory, Seattle, WA 98103 USA
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Jun 11, 2026Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana +2Wildfire Prediction Neural Cellular Automata
Space Data Frontiers Research Center, Fujitsu Research of America, 4655 Great America Pkwy, Santa Clara, 95054, California, USA.
Neural-Parameterized Cellular Automata for Wildfire Spread
Jun 10, 2026Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala +2Wildfire Prediction Wildfire
Fujitsu Research of America, 4655 Great America Pkwy, Santa Clara, 95124, CA, USA
Does Your Wildfire Prediction Model Actually Work, or Just Score Well?
May 14, 2026Yangshuang Xu, Yuyang Dai, Liling Chang +2Wildfire Prediction Wireless Foundation Models
Florida State University · Northeastern University
PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift
May 12, 2026Enyi Jiang, Wu SunWildfire Prediction Rare Events
Siebel School of Computing and Data Science, University of Illiois at Urbana-Champaign · Computer Science, Stanford University · Department of Global Ecology, Carnegie Institution for Science
Set Prediction for Next-Day Active Fire Forecasting
May 11, 2026Yuchen Bai, Georgios Athanasiou, Xin Yu +4Wildfire Prediction
Max Planck Institute for Biogeochemistry, Jena, Germany · Orion Lab, National Technical University of Athens · University of Utah, Salt Lake City, USA +4
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
May 6, 2026Saghar Zolfaghari, Yu Xie, Junfeng Yang +1Combustion Control Multi-Fidelity
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA · School of Mechanical Engineering, University of Leeds, Leeds, UK
Low-Rank Adaptation of Geospatial Foundation Models for Wildfire Mapping Using Sentinel-2 Data
May 6, 2026Ali Shibli, Andrea Nascetti, Yifang BanGeospatial Foundation Models Wildfire Prediction
KTH Royal Institute of Technology Stockholm, Sweden
Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
May 6, 2026Leonard Boussioux, Alexandre Jacquillat, Ryne Reger +1Wildfire Prediction Wildfire
Michael G. Foster School of Business and Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA · Operations Research Center and Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA
Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
May 5, 2026Zachary Morrow, Joseph Crockett, John D. Jakeman +1Wildfire Prediction Atmospheric Dynamics
Sandia National Laboratories 1515 Eubank Blvd SE, Albuquerque, NM 87123
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
May 4, 2026Jonas V. FunkWildfire Prediction Wildfire
Independent Research 76199 Karlsruhe, Germany
Community-Led AI Integration for Wildfire Risk Assessment: A Participatory AI Literacy and Explainability Integration (PALEI) Framework in Los Angeles, CA
Apr 20, 2026Sanaz Sadat Hosseini, Mona Azarbayjani, Mohammad Pourhomayoun +1Wildfire Prediction Artificial Intelligence Literacy
The University of North Carolina at Charlotte, Charlotte, NC · California State University, Los Angeles, CA
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
Jun 10, 2025Hugo Porta, Emanuele Dalsasso, Jessica L. McCarty +1Wildfire Prediction Multimodal Deep Learning
EPFL, Route des Ronquos 86, Sion, 1950, Wallis, Switzerland · Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, 38000, France · NASA Ames Research Center, Earth Science Division„ Moffett Field, California, 94035, USA

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Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction

Zhisheng Qi, Li Zhu, Utkarsh Sahu, Douglas Tommey+2

Wildfire Prediction

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Post-wildfire debris flows (PFDFs) are destructive sediment-laden hazards triggered when intense rainfall strikes recently burned terrain, destabilizing hillslopes and threatening infrastructure, local economies, and community safety. Data-driven methods have been proposed to learn predictive patterns directly from historical PFDF observations. However, the current research landscape of data-driven PFDF prediction remains highly fragmented across feature spaces, model architectures, and evaluation protocols, making rigorous comparison and the derivation of scientific insights difficult. Moreover, existing studies lack a systematic investigation into the relative importance of heterogeneous factors (e.g., meteorological conditions, terrain characteristics, soil properties, and burn severity) in triggering PFDF. To address these limitations, we present a unified benchmark for data-driven PFDF prediction, enabling fair and comprehensive evaluation across diverse models and feature configurations. Furthermore, to better understand the underlying drivers of PFDF formation, we propose a reinforcement learning-based feature selection framework that identifies factors whose perturbations render positive and negative events indistinguishable, thereby discovering the regional underlying mechanisms of PFDF occurrence across regions. Our code and benchmark are publicly available at https://github.com/KINDLab-Fly/PFDF-Benchmark.

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