Detection and attribution of changes in global wildfire activity to anthropogenic drivers using machine learning

Project Details

Description

Exceptional wildfire activity in recent years highlighted the
catastrophic impact these extreme events have on communities,
ecosystems, and economies. Despite the growing concerns over
wildfire activity under continued climate warming, there is little
scientific evidence causally linking observed wildfire changes to
anthropogenic forcings, climatic or non-climatic. In addition, future
projections of burned area are marked by large uncertainties. This
project will combine machine learning with detection and attribution
techniques to uncover the anthropogenic imprint on past and future
global wildfire activity. As attribution studies on wildfire activity are
hampered by the absence of a long-term burned area record, I will
first develop a global burned area reconstruction by applying deep
learning to satellite observations, climate reanalyses and
socioeconomic datasets. Subsequently, trend detection and
attribution will be performed via optimal fingerprinting on new
simulations from global wildfire models. Third, pattern recognition
and attribution methods will be fused to link spatial wildfire patterns to
climate or management changes. Finally, I will deploy machine
learning architectures on a multi-model ensemble of global wildfire
simulations to constrain regional burned area projections across the
globe. The results of this research will deepen scientific
understanding of the human imprint on wildfire and will inform climate
change mitigation and adaptation strategies.
AcronymFWOTM1152
StatusActive
Effective start/end date1/11/2231/10/26

Keywords

  • Evolution of global wildfire activity
  • Machine learning
  • Detection & attribution

Flemish discipline codes in use since 2023

  • Machine learning and decision making
  • Neural, evolutionary and fuzzy computation
  • Climatology
  • Natural hazards
  • Climate change

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