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BEGIN:VEVENT
DTSTAMP:20260428T064826Z
DTSTART;VALUE=DATE-TIME:20200120T150000
DTEND;VALUE=DATE-TIME:20200120T160000
SUMMARY:Jiajia Liu (Sheffield): Application of Machine Learning Technique
 s in CME Arrival Time Prediction
TZID:Europe/London
UID:20200120-8a17841a6e93ba96016ea2d7171f3c4e@warwick.ac.uk
CREATED:20200109T155055Z
DESCRIPTION:Coronal Mass Ejections (CMEs) are one of the most violent eru
 ptions in the Solar system. They can cause severe disturbances of the Ea
 rth's magnetosphere and further affect the operation and working of high
 -tech facilities like spacecraft\, are serious health hazards for astron
 auts\, can cause disruption in functioning of modern communication syste
 ms (including radio\, TV and mobile signals)\, navigation systems\, and 
 affect the working of pipelines and power grids. Fast and accurate predi
 ction of CME arrival time is then vital to minimize the losts CMEs may c
 ost when hitting the Earth. Here\, we present a new model for (partial-)
  halo CME Arrival Time Prediction Using MAchine learning methods (CAT-PU
 MA). Via detailed analysis of the CME features and solar wind parameters
 \, we build the model taking advantage of 182 previously observed geo-ef
 fective (partial-) halo CMEs using the Support Vector Machines (SVMs). T
 he model results show that CAT-PUMA is accurate and fast. In particular\
 , i) predictions using the model on a test set\, that is unknown to our 
 model\, shows a mean absolute prediction error of less than 6 hours of t
 he CME arrival time. Comparisons with other models reveal that CAT-PUMA 
 has a more accurate prediction for 73% events\; ii) the prediction can b
 e carried out within minutes after providing the necessary input paramet
 ers of a CME. The engine of the CAT-PUMA\, with a very user-friendly Use
 r Interface (UI)\, is provided at https://github.com/PyDL/cat-puma. This
  cross-platform tool allows the community to perform their own fast and 
 accurate predictions of CME arrival time using the CAT-PUMA model. Furth
 er\, we have developed a Convolution Neural Network using a single image
  of the SOHO LASCO C2 observations as input to make arrival time predict
 ions of CMEs. It has been shown that\, the average prediction error is a
 round 12.5 hours\, comparable to the average performance of previous stu
 dies on this subject. A comparison between our CNN model and traditional
  methods available at the NASA CME Scoreboard has been performed. It is 
 revealed that\, in 63% events\, our model gives less prediction errors t
 han the average of all traditional models used.
LOCATION:PS128
CATEGORIES:CFSA Seminar
LAST-MODIFIED:20200109T155055Z
ORGANIZER;CN=Anne-Marie Broomhall:
END:VEVENT
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