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DTSTAMP:20220812T074334Z
LOCATION:Foyer 2nd Floor
DTSTART;TZID=Europe/Stockholm:20220628T090000
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UID:submissions.pasc-conference.org_PASC22_sess181_pos157@linklings.com
SUMMARY:P34 - Deep Learning-Based Forecast of Space Weather Indices
DESCRIPTION:Poster\n\nP34 - Deep Learning-Based Forecast of Space Weather 
 Indices\n\nWilliams, Markidis\n\nSpace weather science is an important eme
 rging field investigating events and processes developing in space between
  the Sun and the Earth. The development of space weather forecasting capab
 ilities is a crucial benefit to design strategies to protect human assets 
 in space and on the Earth; as space weather events may damage power lines,
  transformers, pipelines and disrupt communication. Space weather indices,
  such as the Disturbance Storm Time (Dst) index, characterizes magnetic ac
 tivity in Earth’s ring current and aids in identifying geomagnetic storms.
  This work first establishes a time series dataset consisting of historica
 l Dst data and spacecraft observations. We then used the Temporal Fusion T
 ransformer deep-learning architecture for a 12-hour forecast of the Dst in
 dex and compared it to other deep-learning and other traditional approache
 s in terms of accuracy and computational performance. We demonstrated that
  Temporal Fusion Transformers deep-learning networks are an emerging and p
 romising technology for application to space weather.
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