Prediction of the Dst index from solar wind parameters by a neural network method

Watanabe, S.; Sagawa, E.; Ohtaka, Kazuhiro; Shimazu, H.

doi:10.1186/bf03352454

Cited by 27 publications

(14 citation statements)

References 21 publications

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“…Yu et al (2006) proposed a chaos game representation of Dst index while Drezet et al (2002) presented a Kernel based technique. The most notable prediction is achieved by Artificial Neural Network method (Elman 1990;Kugblenu et al 1999;Lundstedt and Wintoft 1994;Lundstedt et al 2002;Pallocchia et al 2006a;Stepanova and Pérez 2000;Watanabe et al 2002;Wu and Lundstedt 1996). All these methods have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 97%

On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index

Banerjee¹,

Bej²,

Chatterjee³

2011

Astrophys Space Sci

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The Dst (Disturbance storm time) index is a measurement of earth geomagnetic activity and is widely used to characterize the geomagnetic storm. It is calculated on the basis of the average value of the horizontal component of the earth's magnetic field at four observatories, namely, Hermanus (33.3°south, 80.3°in magnetic dipole latitude and longitude), Kakioka (26.0°north, 206.0°), Honolulu (21.0°n orth, 266.4°), and San Juan (29.9°north, 3.2°) and is expressed in nano-Teslas. The strength of the low-latitude surface magnetic field is inversely proportional to the energy content of the ring current around earth caused by solar protons and electrons, which increases during geomagnetic storms. Thus a negative Dst index value indicates that the earth's magnetic field is weakened which is specifically the case during solar storms. Predicting Dst index is a difficult task due to its structural complexity involving a variety of underlying plasma mechanism. For characterizing and forecasting this complex time series, a formal model must be established to identify the specific pattern of the series. Persistent demand for a fool proof model of Geomagnetic Dst index prompted us to investigate the Dst Time Series mechanism with a very recent technique called Visibility Algorithm and it is observed that the Dst time series follows the same model that of a Stochastic Fractional Brownian motion having long range correlation.

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Section: Introductionmentioning

confidence: 97%

On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index

Banerjee¹,

Bej²,

Chatterjee³

2011

Astrophys Space Sci

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“…Other techniques for Dst-index predictions include the usage of neural networks, with delayed inputs, with feed-back connections (e.g., Lundstedt and Wintoft 1994;Lundstedt et al 2001;Watanabe et al 2002;Pallocchia et al 2006;Bala and Reiff 2012;Dolenko et al 2014;Lu et al 2016). Bala and Reiff (2012) use the solar wind-magnetosphere coupling function, the Boyle index (Boyle et al 1997), which is an empirical approximation for the polar cap potential dependent on solar wind parameters, as basis functions to train an artificial neural network to predict the Dst-index.…”

Section: Dst-index As a Storm Indicator Measure And Predictormentioning

confidence: 99%

Space Weather Effects Produced by the Ring Current Particles

2017

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One of the definitions of space weather describes it as the time-varying space environment that may be hazardous to technological systems in space and/or on the ground and/or endanger human health or life. The ring current has its contributions to space weather effects, both in terms of particles, ions and electrons, which constitute it, and magnetic and electric fields produced and modified by it at the ground and in space. We address the main aspects of the space weather effects from the ring current starting with brief review of ring current discovery and physical processes and the Dst-index and predictions of the ring current and storm occurrence based on it. Special attention is paid to the effects on satellites produced by the ring current electrons. The ring current is responsible for several processes in the other inner magnetosphere populations, such as the plasmasphere and radiation belts which is also described. Finally, we discuss the ring current influence on the ionosphere and the generation of geomagnetically induced currents (GIC).

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“…Caswell () used B x , B y , and B z components of the IMF, solar wind proton density, solar wind plasma speed, and plasma flow pressure to predict the Dst index 1 hr in advance. Similarly, Watanabe et al () used the solar wind velocity, density, and IMF B x , B y , B z , and B t to predict the Dst index 2 hr in advance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network

et al. 2018

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In this work, we propose an artificial neural network (ANN) with seven input parameters for the prediction of disturbance storm time (Dst) index 1 to 12 hr ahead. The ANN uses past near‐Earth solar wind parameter values to forecast the Dst. The input parameters are the solar wind interplanetary magnetic field, north‐south component of interplanetary magnetic field, temperature, density, speed, pressure, and electric field. The ANN was trained on the data period from 1 January 2007 to 31 December 2015, which contains 78,888 hourly data samples. While the period from 1 January 2016 to 31 May 2017 was used to test the prediction capabilities of the ANN. Several ANN structures were tested and the best results were determined using the correlation coefficient (R) during the training and prediction phases. The results indicate an adequate accuracy of R = 0.876 for prediction 2 hr in advance and R = 0.857 for prediction 12 hr in advance. The power of the proposed ANN was illustrated using the strongest six storms recorded during the prediction period. Generally, the duration and number of the input parameters significantly affect the training and prediction performance of the applied ANN. The results are outstanding in term of accuracy when considering a medium‐term prediction of 12 hr in advance and in terms of timing of the Dst minimum occurrence. In addition, the results show a strong dependence on the solar wind electric current.

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Prediction of the Dst index from solar wind parameters by a neural network method

Cited by 27 publications

References 21 publications

On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index

On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index

Space Weather Effects Produced by the Ring Current Particles

Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network

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