Solar flare forecasting model supported with artificial neural network techniques

Wang, H.N.; Cui, Yanmei; Li, R.; Zhang, L.; Han, Hu

doi:10.1016/j.asr.2007.06.070

Cited by 57 publications

(44 citation statements)

References 14 publications

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“…The headquarters of RWC- Table 1 for a summary of the four subcenters, and note a minor adjustment to the subcenter names of IDPC and GSPC in contrast to the RWC-Beijing era; the service areas and the current web pages of the subcenters are also given in Table 1.) Meanwhile, more institutions and colleges have become involved in the space weather community in China (Wei et al, 2003;Wang and Wang, 2006). In particular, the China Meteorological Administration (CMA) contributes to the discussion of space weather in the World Meteorological Organization (WMO) and the International Civil Aviation Organization (ICAO) as a representative of China; the Polar Research Institute of China (PRIC) also has a wealth of activities related to space weather research.…”

Section: Rwc-china In the New Centurymentioning

confidence: 99%

“…In particular, the China Meteorological Administration (CMA) contributes to the discussion of space weather in the World Meteorological Organization (WMO) and the International Civil Aviation Organization (ICAO) as a representative of China; the Polar Research Institute of China (PRIC) also has a wealth of activities related to space weather research. The efforts of the whole space weather community provide valuable observational and scientific support for the operational activities of RWC-China (Wang and Wang, 2006). At the beginning of the new century, several new solarterrestrial observation instruments and projects were proposed and constructed.…”

Section: Rwc-china In the New Centurymentioning

confidence: 99%

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A brief history of Regional Warning Center China (RWC-China)

Wang

et al. 2018

Hist. Geo Space. Sci.

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Abstract. Solar-terrestrial prediction services in China began in 1969 at the Beijing Astronomical Observatory (BAO), Chinese Academy of Sciences (CAS). In 1990, BAO joined the International URSIgram and World Days Service (IUWDS) and started solar-terrestrial data and prediction interchanges with other members of IUWDS. The short-term solar activity prediction service with standard URSIgram codes began in January 1991 at BAO, and forecasts have been issued routinely every weekday from then on. The Regional Warning Center Beijing (RWC-Beijing) of IUWDS was officially approved in China in 1991 and was formally established in February 1992. In 1996, the IUWDS was changed to the current name, the International Space Environment Service (ISES). In 2000, the RWC-Beijing was renamed RWC-China according to ISES requirements. In 2001, the National Astronomical Observatories, CAS (NAOC) was established. All the solar-terrestrial data and prediction services of BAO were taken up by NAOC. The headquarters of RWC-China is located on the campus of NAOC.

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Section: Rwc-china In the New Centurymentioning

confidence: 99%

Section: Rwc-china In the New Centurymentioning

confidence: 99%

A brief history of Regional Warning Center China (RWC-China)

Wang

et al. 2018

Hist. Geo Space. Sci.

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“…We find forecasting methods using Support Vector Machines [9,12,13], Artificial Neural Networks [7,10,12,14], C4.5 decision trees [5,11,[15][16][17], Naive Bayes [12] and Bayesian Networks [15]. The majority of the works tries different classifiers, looking for the one that achieves the best results according to the adopted criteria.…”

Section: Related Workmentioning

confidence: 99%

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

et al. 2018

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X-rays emitted by the Sun can damage electronic devices of spaceships, satellites, positioning systems and electricity distribution grids. Thus, the forecasting of solar X-rays is needed to warn organizations and mitigate undesirable effects. Traditional mining classification methods categorize observations into labels, and we aim to extend this approach to predict future X-ray levels. Therefore, we developed the "SeMiner" method, which allows the prediction of future events. "SeMiner" processes X-rays into sequences employing a new algorithm called "Series-to-Sequence" (SS). It employs a sliding window approach configured by a specialist. Then, the sequences are submitted to a classifier to generate a model that predicts X-ray levels. An optimized version of "SS" was also developed using parallelization techniques and Graphical Processing Units, in order to speed up the entire forecasting process. The obtained results indicate that "SeMiner" is well-suited to predict solar X-rays and solar flares within the defined time range. It reached more than 90% of accuracy for a 2-day forecast, and more than 80% of True Positive (TPR) and True Negative (TNR) rates predicting X-ray levels. It also reached an accuracy of 72.7%, with a TPR of 70.9% and TNR of 79.7% when predicting solar flares. Moreover, the optimized version of "SS" proved to be 4.36 faster than its initial version.

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“…To forecasting a flare event is usually converted to classify one sample as a flaring sample or a non-flaring sample. Previously, researchers usually adopt support vector machines, such as Qahwaji & Colak (2007), or neural networks, such as Wang et al (2008). The outputs of support vector machines and neural networks are binary labels indicating flaring or nonflaring.…”

Section: Introductionmentioning

confidence: 99%

Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters

et al. 2010

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Abstract. In this paper, we investigate whether incorporating sunspot-groups classification information would further improve the performance of our previous logistic regression based solar flare forecasting method, which uses only line-of-sight photospheric magnetic parameters. show that sunspot-groups classification combined with total gradient on the strong gradient polarity neutral line achieve the highest forecasting accuracy and thus it testifies sunspot-groups classification does help in solar flare forecasting.

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Solar flare forecasting model supported with artificial neural network techniques

Cited by 57 publications

References 14 publications

A brief history of Regional Warning Center China (RWC-China)

A brief history of Regional Warning Center China (RWC-China)

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters

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