Session 11 Artificial Intelligence/Machine Learning Applications to Fire II

Thursday, 4 May 2023: 1:30 PM-3:00 PM
Scandinavian Ballroom Salon 4 (Royal Sonesta Minneapolis Downtown )
Host: 14th Fire and Forest Meteorology Symposium
Chair:
Wei-Ting Hung

Papers:
1:30 PM
11.1
Exploring the Impact of Modeling Choices on Model Performance when Predicting Wildfire Occurrence for CONUS Using the Unet3+ Deep Learning Model
Bethany Earnest, CIWRO, Norman, OK; CIWRO, Univ. of Oklahoma, Norman, OK; and A. McGovern, C. Karstens, and I. L. Jirak

1:45 PM
11.2
Remote Sensing of Live Fuel Moisture Content Using Machine Learning
Angel Farguell, San Jose State Univ., San Jose, CA; and A. Kochanski, J. Drucker, Y. Moon, C. Bowers, D. Pina, C. Arends, and B. D'Augustino

2:00 PM
11.3
Using Hybrid AI for Multiscale Wildfire Risk Assessment and Mitigation
Jared Michael Goldman, Charles River Analytics, Cambridge, MA; and A. Pfeffer, S. Cvijic, N. McGeorge, and M. Hiett

2:15 PM
11.4
A Classification Model for Daily Lightning Intensities in Alaska
Joshua Hostler, Univ. of Alaska Fairbanks, Fairbanks, AK; and U. S. Bhatt, P. Bieniek, E. Fischer, T. J. Ballinger, C. Borries-Strigle, R. Thoman, R. Lader, M. Burgard, C. F. Waigl, J. Chriest, H. Strader, E. Stevens, and Z. Parish

2:30 PM
11.5
Development and Evaluation of a Machine Learning Based Wildfire Spread Prediction Model for Regional Air Quality Forecasting
Wei-Ting Hung, ARL, College Park, MD; George Mason Univ., Fairfax, VA; and B. Baker, P. C. Campbell, Y. Tang, G. R. Jeong, and Z. Moon

2:45 PM
11.6
A Fast Fire Rate of Spread Model Leveraging Machine Learning
Adam Kochanski, San Jose State Univ., San Jose, CA; and A. Farguell, J. Drucker, and J. Mandel

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- Indicates paper has been withdrawn from meeting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