Wildfire Spread Forecasting Solutions

Sun, Ziheng

A107
Wildfire Spread Forecasting Solutions

Session ID#: 248202

Session Description:

Wildfires are growing in frequency, intensity, and impact—posing an urgent threat to ecosystems, communities, and infrastructure worldwide. This session invites submissions that explore practical, scalable, and actionable approachesto forecasting wildfire spread using advanced geospatial methods, AI/ML models, and integrated sensing technologies.We especially welcome contributions that go beyond theoretical modeling and demonstrate real-world impact through field deployment, decision support, or community engagement. Topics may include, not limited to: Data-driven models for near-term wildfire spread predictionIntegration of remote sensing, meteorological, and topographic dataAI/ML approaches for fire risk forecastingReal-time systems for situational awareness and responseVisualization and user interfaces for decision-makersCommunity-based or low-cost forecasting innovationsEvaluation frameworks and metrics (e.g., accuracy, timeliness)Open-source tools and interoperable solutionsWhether you're building algorithms, deploying tools, or shaping policy, we invite you to share your work in this timely conversation. Let’s build forecasting tools that work not just in the lab—but where and when they’re needed most.

Co-Sponsor(s):

GC - Global Environmental Change
GH - GeoHealth
IN - Informatics
NH - Natural Hazards

Index Terms:

0350 Pressure, density, and temperature [ATMOSPHERIC COMPOSITION AND STRUCTURE]
3390 Wildland fire model [ATMOSPHERIC PROCESSES]
4315 Monitoring, forecasting, prediction [NATURAL HAZARDS]
4341 Early warning systems [NATURAL HAZARDS]

Primary Convener: Ziheng Sun, George Mason University Fairfax, Fairfax, VA, United States

Conveners: Daniel Tong, George Mason University, Department of Atmospheric, Oceanic & Earth Sciences, Fairfax, United States, Yunyao Li, University of Maryland College Park, College Park, United States and Li Zhang, CU Boulder/CIRES, Boulder, United States

Analysis of Daily Weather Variable Correlations with the Size and Severity of the Largest Recent Wildfires in the Pacific States of California, Oregon and Washington (1849607)

Steven Vasquez¹, Christopher S Potter², Stephanie Pass³, Wing To⁴ and Jeffrey Mirocha¹, (1)California State University Stanislaus, Turlock, United States, (2)NASA Ames Research Center, Moffett Field, CA, United States, (3)Bay Area Environmental Research Institute, Mountain View, United States, (4)California State University Stanislaus, Department of Physics, Physical Sciences and Geology, Turlock, United States

Updates of Fire Intensity and spRead forecAst (FIRA): Effects of Dynamic Fuel Fraction on Fire Spread Prediction (1855448)

Wei-Ting Hung¹, Barry Baker², Patrick C Campbell³ and Youhua Tang², (1)George Mason University, Fairfax, United States, (2)NOAA Air Resources Laboratory, College Park, United States, (3)NOAA Air Resources Laboratory & George Mason University, College Park, United States

Advancing Wildfire Detection Through Lightweight Attention Mechanisms in YOLOv8n Architectures (1857734)

Peiman Parisouj¹, Hooman Aghalou² and Sayed M. Bateni¹, (1)University of Hawaii at Manoa, Department of Civil and Environmental Engineering and Water Resources Research Center, Honolulu, HI, United States, (2)K. N. Toosi University of Technology, Department of Photogrammetry and Remote Sensing, Tehran, Iran

Satellite-derived wildfire smoke plume height climatology across CONUS (1862204)

Shuai An¹, Xiaoyang Zhang¹, Fangjun Li¹, Mark A. Cochrane² and Shobha Kondragunta³, (1)South Dakota State University, Geospatial Sciences Center of Excellence (GSCE), Department of Geography & Geospatial Sciences, Brookings, United States, (2)University of Maryland Center for Environmental Science Appalachian Laboratory, Frostburg, MD, United States, (3)NOAA/NESDIS/Center for Satellite Applications and Research, College Park, United States

Importance of accounting for fire/atmosphere feedbacks in wildland fire modeling (Invited) (1866069)

Rodman Linn¹, Kevin Hiers², David Robinson³, Joseph O'Brien⁴, Kara M Yedinak⁵, Zachary Crennen⁶, Chad M Hoffman⁷, Jesse Canfield³, Alexander Josephson³, Adam L Atchley⁶ and Scott Goodrick⁸, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)DoD SERDP & ESTCP, Alexandria, United States, (3)Los Alamos National Laboratory, Los Alamos, United States, (4)USDA Forest Service Southern Research Station, Athens Prescribed Fire Science Laboratory, Athens, United States, (5)USDA Forest Service, Northern Research Station, Madison, United States, (6)Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, United States, (7)Colorado State University, Forest and Rangeland Stewardship Program, Fort Collins, CO, United States, (8)US Forest Service Athens, Athens, United States

FRP based Global Wildfire Predictions Using Deep Learning (1866550)

Tong WU, The Hong Kong University of Science and Technology (Guangzhou), Washington, United States, Junyu Zheng, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China and Zhijiong Huang, Jinan University, Institute for Environmental and Climate Research, Guangzhou, China

Improving Wildfire Modeling with Artificial Intelligence and Data Driven Techniques (1880007)

Nipun Gunawardena, Lawrence Livermore National Laboratory, Livermore, CA, United States and Donald D Lucas, LLNL, Livermore, United States

ASSESSMENT OF WILDFIRE PROPAGATION RISK IN URBAN FRINGE FORESTS BASED ON LIDAR-DERIVED FUEL STRUCTURE AND TOPOGRAPHY (1890642)

Yuhan Zhang¹, Zipeng Song¹ and Youngkeun Song², (1)Seoul National University, Interdisciplinary Program in Landscape Architecture, Seoul, South Korea, (2)Seoul National University, Seoul, South Korea

Experimental Analysis of Fire Turbulence and Ember Lofting for Wildfire Modeling (1894385)

Alexander Ordentlich¹, Antonio Cervantes², Tirtha Banerjee² and Alec Petersen², (1)Bowdoin College, Brunswick, ME, United States, (2)University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States

Influence of Built Environment Characteristics on Fire Spread: A Case Study of the Eaton Fire (1911847)

Negar Elhami Khorasani, State University of NY at Buffalo, Department of Civil, Structural, and Environmental Engineering, Buffalo, United States, Patrick Hadinata, University of California, Los Angeles, Los Angeles, United States, Fernando Jose Szasdi Bardales, University at Buffalo, Civil, Structural and Environmental Engineering, Buffalo, NY, United States, Weichen LIU, University of Wyoming, Laramie, WY, United States, Nisha Saharan, University at Buffalo, Buffalo, United States, Riyaaz Uddien Shaik, University of California, Los Angeles, Civil and Environmental Engineering, Los Angeles, United States, Alex Hall, University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, United States, Timothy W Juliano, National Center for Atmospheric Research, Boulder, United States, Ertugrul Taciroglu, University of California Los Angeles, Department of Civil and Environmental Engineering, Los Angeles, United States, Maria Frediani, National Center for Atmospheric Research, Boulder, CO, United States and Stefan Rahimi, University of Wyoming, Atmospheric Science, Laramie, United States

Developing regional-scale wildfire activity forecasts for air quality predictions (1915708)

Taylor Wilmot, Trace Air Quality, Salt Lake City, United States, Derek V. Mallia, University of Utah, Atmospheric Sciences, Salt Lake City, United States and Heather Holmes, Department of Chemical Engineering, University of Utah, Salt Lake City, United States

Development of an Machine Learning (ML)-Based Wildfire Emission Prediction System for Weather and Subseasonal-to-Seasonal (S2S) Forecasting (1922717)

Li Zhang¹, Gonzalo Ferrada¹, Ziheng Sun², Yunyao Li³, Shan Sun⁴ and Daniel Tong⁵, (1)CU Boulder/CIRES and NOAA Global Systems Laboratory, Boulder, United States, (2)George Mason University, Center for Spatial Information Science and Systems, Fairfax, VA, United States, (3)The University of Texas at Arlington, Arlington, United States, (4)NOAA Global Systems Laboratory, Boulder, United States, (5)George Mason University, Department of Atmospheric, Oceanic & Earth Sciences, Fairfax, United States

Modeling the Eaton Fire through the Wildland and Urban Landscapes (1923296)

Negar Elhami Khorasani, State University of NY at Buffalo, Department of Civil, Structural, and Environmental Engineering, Buffalo, United States, Patrick Hadinata, University of California, Los Angeles, Los Angeles, United States, Fernando Jose Szasdi Bardales, University at Buffalo, Civil, Structural and Environmental Engineering, Buffalo, NY, United States, Weichen LIU, University of Wyoming, Laramie, WY, United States, Nisha Saharan, University at Buffalo, Buffalo, United States, Riyaaz Uddien Shaik, University of California, Los Angeles, Civil and Environmental Engineering, Los Angeles, United States, Alex Hall, University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, United States, Timothy W Juliano, National Center for Atmospheric Research, Boulder, United States, Ertugrul Taciroglu, University of California Los Angeles, Department of Civil and Environmental Engineering, Los Angeles, United States, Maria Frediani, National Center for Atmospheric Research, Boulder, CO, United States and Stefan Rahimi, University of Wyoming, Atmospheric Science, Laramie, United States

Toward Field-Scale, Daily Wildfire Risk Forecasting: Integrating Remote Sensing, Modeled Soil Moisture and Biomass, and One-Class Machine Learning (1932176)

James A. Zollweg, SUNY College at Brockport, Brockport, NY, United States and Erik Winarski, SUNY Brockport, Brockport, United States

Time-Resolved Moving‑Window Machine Learning Modeling for Human‑Caused Wildfire Ignition Prediction in California (1936871)

Zhijiong Cao, University of California Los Angeles, Los Angeles, CA, United States, Yongkang Xue, Univ California Los Angeles, Los Angeles, United States, Fangshu Ye, Iowa State University, Ames, United States and Thomas W Gillespie, University of California, Los Angeles (UCLA), Department of Geography, Los Angeles, United States

Evaluating Wildfire Emissions and Plume Rise Using WRF-Chem: A Case Study of the 2019 Williams Flats Fire (1943719)

Soo Keun RO, Yonsei University, Department of Atmospheric Sciences, Seoul, Korea, Republic of (South) and Young-Hee Ryu, Yonsei University, Department of Atmospheric Sciences, Seoul, South Korea

Advances in the Unified Forecast System & Short-Range Weather App: Fire Behavior & Smoke/Dust Modeling Capabilities (1951706)

Jong Kim¹, Benjamin W Koziol², Michael Kavulich Jr³, Johana Romero-Alvarez⁴, Chan-hoo Jeon⁵, Pedro Jiménez⁶, Ravan Ahmadov⁷, Masih Eghdami⁶, Dan Rosen⁸, Michael Lueken⁹ and Maria Frediani⁶, (1)Science and Technology Corporation, Boulder, CO, United States, (2)NOAA/EPIC, College Park, United States, (3)Developmental Testbed Center (DTC), Boulder, United States, (4)CIRES/NOAA GSL, Boulder, United States, (5)Science and Technology Corporation, Columbia, United States, (6)National Center for Atmospheric Research, Boulder, CO, United States, (7)NOAA Global Systems Laboratory, Boulder, United States, (8)National Center for Atmospheric Research, Boulder, United States, (9)Tomorrow.io, Silver Spring, United States

Conditional Score-Based Diffusion Models for Probabilistic Prediction of Wildfire Spread in Sub-Wildfire-Scale Regions (1964612)

Bryan Shaddy¹, Haitong Qin², Brianna Binder¹, Agnimitra Dasgupta¹, James Haley, PhD³, Angel Farguell⁴, Kyle Hilburn³, Derek V. Mallia⁵, Adam Kochanski⁴, Jan Mandel⁶ and Assad Oberai¹, (1)University of Southern California, Department of Aerospace and Mechanical Engineering, Los Angeles, United States, (2)University of Southern California, Department of Mathematics, Los Angeles, United States, (3)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, United States, (4)San Jose State University, Wildfire Interdisciplinary Research Center, San Jose, United States, (5)University of Utah, Atmospheric Sciences, Salt Lake City, United States, (6)University of Colorado Denver, Mathematical and Statistical Sciences, Denver, CO, United States

Forecasting Subseasonal Fire Radiative Power with Novel TabNet-GPR and GNN-LSTM (1969196)

Sanat Rao¹, Shriya Natarajan², Ishan Nagpal³, Harshit Singh³ and Ziheng Sun⁴, (1)Ardrey Kell High School, Charlotte, NC, United States, (2)Wilton High School, Wilton, CT, United States, (3)Edison Academy Magnet School, Edison, NJ, United States, (4)George Mason University, Center for Spatial Information Science and Systems, Fairfax, VA, United States

Machine Learning to Advance Fire Spread Modeling (Invited) (1969513)

James Tremper Randerson, University of California Irvine, Earth System Science, Irvine, United States

Forecasting Post-Wildfire Vegetation Recovery Dynamics Using a Spatiotemporal Vision Transformer (1978949)

Svaran Medavarapu, Thomas Jefferson High School for Science And Technology, Alexandria, United States and Ziheng Sun, George Mason University, Center for Spatial Information Science and Systems, Fairfax, VA, United States

Coupled Interactive Forecasting of Wildfire Potential and Active Fire Impacts (1983531)

Kyle Hilburn, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, James Haley, PhD, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, United States, Jan Mandel, University of Colorado Denver, Mathematical and Statistical Sciences, Denver, CO, United States and Adam Kochanski, San Jose State University, Wildfire Interdisciplinary Research Center, San Jose, United States

Assessing the Role of Drought and Wind in Driving the Extreme 2025 Ofunato Wildfire, Japan (1991451)

Chenling Sun¹, Yoshiya Touge² and Kenji Tanaka², (1)Kyoto University, Graduate School of Engineering, Kyoto, Japan, (2)Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan

Assessing the Role of Climate Variables in Wildfire Emergence in the Eastern United States (2002223)

Melanie Hein, Lycoming College, Environmental Science, Williamsport, United States, Erica A H Smithwick, Pennsylvania State University, Department of Geography and Earth and Environmental Systems Institute, University Park, United States, Mobashsira Tasnim, Organization Not Listed, Washington, DC, United States and Casey Hamilton, Pennsylvania State University, State College, PA, United States

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A107 Wildfire Spread Forecasting Solutions

Session Description:

Co-Sponsor(s):

Index Terms:

A107
Wildfire Spread Forecasting Solutions