AOGS2019 ST20 Solar Flare Forecasting Using Machine Learning

December 3, 2018, from robertus erdelyi

Dear Colleagues,

We would like to draw your attention to Session ST20: Solar Flare Forecasting Using Machine Learning, of the 16th Annual Meeting of the Asia-Oceania Geosciences Society (AOGS), 28 Jul – 02 Aug, 2019, Singapore.

Please note that the deadline for Abstract Submission is 12 Feb 2019. Submission can be made at:

ST20 Session Description:

Solar flares, one of the most powerful and energetic explosions in our Solar System, are often caused by very abrupt and sudden changes of magnetic field configuration in the Sun’s atmosphere. These violent solar activities could be potentially catastrophic to our satellites, ground-based infrastructure, and even threat the health and life of humans. Therefore, solar flare forecasting has drawn considerable attentions from scientists to governments in recent years.

Besides the physical models of solar flare forecasting, there are more and more successful large data-driven models developed on the basis of machine learning methods. Along with the rise of big data, the advantages and potentials of date-driven models became increasingly relevant. This session solicits presentations focusing on a wide variety of solar flare forecasting models, especially those about data-driven models. We particularly encourage submissions on addressing recent results of solar flare forecasting based on machine learning techniques. We would also welcome submissions addressing the design and operation of numerical forecasting of solar flares, delivering a cutting-edge, more reliable, accurate and near-real time automated solar flare forecasting.

Long Xu (NAOC, China),
Robertus Erdelyi (U of Sheffield, UKi),
Xin Huang (NAOC, China),