A neural network model of the relativistic electron flux at geosynchronous orbit

Koons, H. C.; Gorney, D. J.

doi:10.1029/90ja02380

Cited by 107 publications

(80 citation statements)

References 20 publications

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“…Since this study, most of the data analysis research into energetic electrons has been accomplished by neural networks (NNs). The inputs to the NNs are often the geomagnetic indices such as the Dst index and the daily sum of the global geomagnetic index, Kp (Freeman et al, 1998;Ling et al, 2010;Koons and Gorney, 1991). NNs have provided results that are significantly more accurate than those from LPFs; however, the NNs are much more difficult to interpret than the LPFs.…”

Section: Introductionmentioning

confidence: 99%

Online NARMAX model for electron fluxes at GEO

2015

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Abstract. Multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 MeV and > 2 MeV electron fluxes at geostationary Earth orbit (GEO). The NARMAX algorithm is able to identify mathematical model for a wide class of nonlinear systems from input-output data. The models employ solar wind parameters as inputs to provide an estimate of the average electron flux for the following day, i.e. the 1-day forecast. The identified models are shown to provide a reliable forecast for both > 0.8 and > 2 MeV electron fluxes and are capable of providing real-time warnings of when the electron fluxes will be dangerously high for satellite systems. These models, named SNB 3 GEO > 0.8 and > 2 MeV electron flux models, have been implemented online at http://www.ssg.group.shef. ac.uk/USSW/UOSSW.html.

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Section: Introductionmentioning

confidence: 99%

Online NARMAX model for electron fluxes at GEO

2015

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“…Arti®cial neural networks (ANNs) are a branch of AI methods which are proving particularly successful in solar-terrestrial time series prediction and pattern recognition; they appear to be especially e ective in modelling the time development of irregular processes (Koons and Gorney, 1991;Lundstedt, 1992;Gorney et al, 1993;Lundstedt and Wintoft, 1994;Williscroft and Poole, 1996;Wu and Lundstedt, 1996; Sutcli e, 1997; Weigel et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

The development of a regional geomagnetic daily variation model using neural networks

Sutcliffe¹

2000

Ann. Geophys.

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Abstract. Global and regional geomagnetic ®eld models give the components of the geomagnetic ®eld as functions of position and epoch; most utilise a polynomial or Fourier series to map the input variables to the geomagnetic ®eld values. The only temporal variation generally catered for in these models is the long term secular variation. However, there is an increasing need amongst certain users for models able to provide shorter term temporal variations, such as the geomagnetic daily variation. In this study, for the ®rst time, arti®cial neural networks (ANNs) are utilised to develop a geomagnetic daily variation model. The model developed is for the southern African region; however, the method used could be applied to any other region or even globally. Besides local time and latitude, input variables considered in the daily variation model are season, sunspot number, and degree of geomagnetic activity. The ANN modelling of the geomagnetic daily variation is found to give results very similar to those obtained by the synthesis of harmonic coe cients which have been computed by the more traditional harmonic analysis of the daily variation.

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“…This model predicted successfully the electron flux on a daily scale. Koons and Gorney (1991) also made predictions of the daily average flux at GEO using artificial neural networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms

et al. 2002

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Abstract. During the recovery phase of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at geosynchronous orbits is enhanced. This enhancement reaches a level that can cause devastating damage to instruments on satellites. To predict these temporal variations, we have developed neural network models that predict the flux for the period 1-12 h ahead. The electron-flux data obtained during storms, from the Space Environment Monitor on board a Geostationary Meteorological Satellite, were used to construct the model. Various combinations of the input parameters AL, AL, D st and D st were tested (where denotes the summation from the time of the minimum D st ). It was found that the model, including AL as one of the input parameters, can provide some measure of relativistic electron-flux prediction at geosynchronous orbit during the recovery phase. We suggest from this result that the relativistic electron-flux enhancement during the recovery phase is associated with recurring substorms after D st minimum and their accumulation effect.

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A neural network model of the relativistic electron flux at geosynchronous orbit

Cited by 107 publications

References 20 publications

Online NARMAX model for electron fluxes at GEO

Online NARMAX model for electron fluxes at GEO

The development of a regional geomagnetic daily variation model using neural networks

Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms

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