MO3.P1.5
LEVERAGING ADABOOST AND BAYESIAN OPTIMIZATION TO ENHANCE DECISION TREE MODELS FOR WILDFIRE PREDICTION
Rui Jiang, Jingyu Zhang, University of Electronic Science and Technology of China, China
Session:
MO3.P1: Improved Characterization and Understanding of Wildfires and their Environmental Impacts using Satellite Data and Artificial Intelligence I Oral
Track:
Community-Contributed Sessions
Location:
Plaza: Room P1
Presentation Time:
Monday, 4 August, 14:00 - 14:15
Session Co-Chairs:
Jungho Im, Ulsan National Institute of Science & Technology and Marta Yebra, Autralian Național University
Session MO3.P1
MO3.P1.1: Influence of Vegetation Biophysical Properties and Plant Functional Type Composition on Wildfire Behaviors
Somnath Bar, Tirtha Banerjee, University of California-Irvine, United States
MO3.P1.2: Assessment of satellite overpass timing for detection of new unplanned fires
Nicholas Wilson, Australian National University, Australia; Mark de Jong, Canadian Forest Service, Canada; Marta Yebra, Australian National University, Australia
MO3.P1.3: Identifying Key Environmental Predictors of Fire Radiative Power (FRP) Using Multi-Sensor Earth Observation Data
Iqra Munir, Karin J. Reinke, Simon D. Jones, Mariela Soto-Berelov, Royal Melbourne Institute of Technology, Australia
MO3.P1.4: ENHANCING SATELLITE-BASED WILDFIRE MONITORING: ADVANCED CONTEXTUAL MODEL USING ENVIRONMENTAL AND STRUCTURAL INFORMATION
Taejun Sung, Yoojin Kang, Jungho Im, Ulsan National Institute of Science and Technology, Korea (South)
MO3.P1.5: LEVERAGING ADABOOST AND BAYESIAN OPTIMIZATION TO ENHANCE DECISION TREE MODELS FOR WILDFIRE PREDICTION
Rui Jiang, Jingyu Zhang, University of Electronic Science and Technology of China, China
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