DocumentCode :
2939691
Title :
Support Vector Machines for automated knowledge extraction from historical solar data: A practical study on CME predictions
Author :
Al-Omari, Muhannad ; Qahwaji, R. ; Colak, T. ; Ipson, S.
Author_Institution :
Dept. of Electron. Imaging & Media Commun., Univ. of Bradford, Bradford
fYear :
2008
fDate :
20-22 July 2008
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, Associations algorithms and Support Vector Machines (SVM) are applied to analyse years of solar catalogues data and to study the associations between eruptive filaments/prominences and Coronal Mass Ejections (CMEs). The aim is to identify patterns of associations that can be represented using SVM learning rules to enable real-time and reliable CME predictions. The NGDC filaments catalogue and the SOHO/LASCO CMEs catalogue are processed to associate filaments with CMEs based on timing and location information. Automated systems are created to process and associate years of filaments and CME data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Features representing the filament time, duration, type and extent are extracted from all the associated (A) and not-associated (NA) filaments and converted to a numerical format that is suitable for machine learning use. The machine learning system predicts if the filament is likely to initiate a CME. Intensive experiments are carried out to optimise the SVM. The prediction performance of SVM is analysed and recommendations for enhancing the performance are provided.
Keywords :
astronomical image processing; cataloguing; corona; data mining; information retrieval; learning (artificial intelligence); pattern classification; support vector machines; NGDC filaments catalogue; SOHO-LASCO CME catalogue; SVM classification system; associations algorithms; automated knowledge extraction; coronal mass ejection; data mining; historical solar data; machine learning algorithm; numerical training vectors; pattern identification; solar catalogues data; solar imaging; support vector machine; Data mining; Earth; Geomagnetism; Machine learning; Magnetic fields; Plasmas; Space technology; Storms; Support vector machines; Timing; Data mining; SVM; Solar Imaging; information retrieval; machine learning; space weather;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Signals and Devices, 2008. IEEE SSD 2008. 5th International Multi-Conference on
Conference_Location :
Amman
Print_ISBN :
978-1-4244-2205-0
Electronic_ISBN :
978-1-4244-2206-7
Type :
conf
DOI :
10.1109/SSD.2008.4632812
Filename :
4632812
Link To Document :
بازگشت