Short-term Forecast of Solar Proton Events With Characteristic Phyiscal Quantities of Photospheric Magnetic Fields

Cui, Yanmei; Li, Rong; Liu, Siqing

doi:10.1016/j.chinastron.2012.04.004

Cited by 1 publication

(4 citation statements)

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“…It is not easy to compare our model with previous other models in that other neural network models mostly predict 'yes' or 'no' of SPE event (Gong et al 2004;Cui et al 2012). Wang (2000) used a neural network method for the forecast of three levels of SPE flux.…”

Section: Resultsmentioning

confidence: 99%

“…We plan to develop a model based on both flare and CME parameters as its input data. We will also consider other input data such as hard Xray fluxes (Garcia 2004), magnetic properties (Cui et al 2012), and other solar activities (Wang 2000;Gong et al 2004). …”

Section: Discussionmentioning

confidence: 99%

“…The models used several kinds of input data such as sunspot properties from white light and magnetogram observations, flare, and radio burst. While two models (Gong et al 2004;Cui et al 2012) can predict "event" or "no event", Wang (2000) suggested a two-pass model using by both a yes/no event prediction and forecast three levels of SPE events. In this study we make the first attempt to forecast SPE scales by neural network.…”

Section: Introductionmentioning

confidence: 99%

“…ral networks (Wang 2000;Gong et al 2004;Cui et al 2012), which are mostly applied to the prediction of SPE occurrence (Yes or No of SPE events). The models used several kinds of input data such as sunspot properties from white light and magnetogram observations, flare, and radio burst.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Forecast of Solar Proton Events With Noaa Scales Based on Solar X-Ray Flare Data Using Neural Network

Jeong¹,

Lee²,

Moon³

et al. 2014

Journal of The Korean Astronomical Society

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Abstract:In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

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Section: Resultsmentioning

confidence: 99%