J8A Machine Learning for Smoke and Wildfires

Tuesday, 30 January 2024: 4:30 PM-6:00 PM
345/346 (The Baltimore Convention Center)
Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 15th Conference on Weather, Water, Climate, and the New Energy Economy )
Cochairs:
Douglas Rao, Cooperative Institute for Satellite Earth System Studies (CISESS), Asheville, NC and Lora Koenig

This session will highlight abstracts utilizing machine learning methods in applications to aerosols and wildfires. This can range from short- to long term- forecasts.

Papers:
4:30 PM
J8A.1
Leveraging Open-Source Data and Tools to Predict Wildfire Risk Using Machine Learning
Rochelle S Koeberle, Booz Allen Hamilton, Arlington, VA

4:45 PM
J8A.2
Exploring the Role of Weather Forecasts in Predicting Wildfire Occurrence for CONUS Using the Unet3+ Deep Learning Model
Bethany Earnest, CIWRO, Norman, OK; and A. McGovern, C. Karstens, and I. L. Jirak

5:00 PM
J8A.3
Modeling Wildfire Behavior with Forest Machine Learning Models Using the RAVE Dataset
Christina E. Kumler, ; and J. Romero-Alvarez and J. Q. Stewart

5:15 PM
J8A.4
A Machine Learning Rate of Spread Model in WRF-SFIRE
Angel Farguell, San Jose State Univ., San Jose, CA; and J. Drucker, J. Mandel, and A. Kochanski

5:30 PM
J8A.5
Predicting Wildfire Fuel Moisture from Atmospheric Data Using Machine Learning
Pankaj Kumar Jha, LLNL, Livermore, CA; and J. D. Mirocha, A. Farguell, A. Kochanski, and P. Cameron-Smith

Handout (2.0 MB)

5:45 PM
J8A.6
Advancing Wildfire Research Using Large Eddy Simulation (LES) and Machine Learning
Siyuan Wang, CIRES, CU Boulder, Boulder, CO; NOAA, Boulder, CO

Recorded Presentation
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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