2018
DOI: 10.1029/2018sw001863
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Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network

Abstract: 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 co… Show more

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Cited by 27 publications
(16 citation statements)
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“…In particular, machine learning techniques have been used to forecast geomagnetic indices (cf. Morley, 2020), for example the Kp index (e.g., Ji et al, 2013; Tan et al, 2018; Wang et al, 2017; Wing et al, 2005; Wintoft et al, 2017) and Dst/Sym‐H indices (e.g., Bhaskar & Vichare, 2019; Chandorkar et al, 2017; Kugblenu et al, 1999; Lethy et al, 2018; Lundstedt et al, 2002; Wu & Lundstedt, 1996). In this work, we investigate the ability of machine learning methods to provide a probabilistic forecast as to whether an observed interplanetary shock will lead to an SC (e.g., a significant ground magnetic field signature), and further whether this will be followed by a geomagnetic storm (i.e., the shock is related to an SSC).…”
Section: Introductionmentioning
confidence: 99%
“…In particular, machine learning techniques have been used to forecast geomagnetic indices (cf. Morley, 2020), for example the Kp index (e.g., Ji et al, 2013; Tan et al, 2018; Wang et al, 2017; Wing et al, 2005; Wintoft et al, 2017) and Dst/Sym‐H indices (e.g., Bhaskar & Vichare, 2019; Chandorkar et al, 2017; Kugblenu et al, 1999; Lethy et al, 2018; Lundstedt et al, 2002; Wu & Lundstedt, 1996). In this work, we investigate the ability of machine learning methods to provide a probabilistic forecast as to whether an observed interplanetary shock will lead to an SC (e.g., a significant ground magnetic field signature), and further whether this will be followed by a geomagnetic storm (i.e., the shock is related to an SSC).…”
Section: Introductionmentioning
confidence: 99%
“…A direct comparison between the results obtained by the proposed methods and other methods reported in the literature (as presented in Table ) is complicated due to differences in databases and methodologies used in each study (Andriyas & Andriyas, ; Andrejková & Levický, ; Bala & Reiff, ; Barkhatov et al, ; Gleisner et al, ; Gruet et al, ; Jankovičová et al, ; Kugblenu et al, ; Lazzús et al, ; Lethy et al, ; Lotfi & Akbarzadeh‐T., ; Lundstedt & Wintoft, ; Lundstedt et al, ; Munsami, ; Ouarbya & Mirikitani, ; Ouarbya et al, ; Pallocchia et al, ; Revallo et al, ; Sharifi et al, ; Sharifi et al, ; Singh & Singh, ; Stepanova et al, ; Stepanova & Pérez, ; Stepanova et al, ; Vega‐Jorquera et al, ; Vörös & Jankovičová, ; Watanabe et al, ; ; Wei et al, ; Wu & Lundstedt, ; ; Xue & Gong, ). In addition, the ANN configurations contain deep variation such as the time steps of ahead prediction (i.e., output), input parameters, and the number of neurons in the hidden layer (see Table ).…”
Section: Discussionmentioning
confidence: 99%
“…see review by Camporeale, 2019). Often this has taken the form of forecasting a geomagnetic index (Liemohn et al, 2018), for example the Sym-H (Siciliano et al, 2020;Bhaskar & Vichare, 2019), Dst/Est (Chandorkar et al, 2017;Kugblenu et al, 1999;Lethy et al, 2018;Lundstedt et al, 2002;Wu & Lundstedt, 1996;Gruet et al, 2018;Wintoft & Wik, 2018;Tasistro-Hart et al, 2021) or Kp indices (Zhelavskaya et al, 2019;Ji et al, 2013;Tan et al, 2018;Wing et al, 2005;Wintoft et al, 2017). Models have also been produced that aim to predict phenomena such as ionospheric current systems (Kunduri et al, 2020), geomagnetic storms (Chakraborty & Morley, 2020), substorms (Maimaiti et al, 2019) or SSCs (Smith, Rae, Forsyth, Oliveira, et al, 2020).…”
Section: Accepted Articlementioning
confidence: 99%
“…In the last 5–10 years machine learning methods have been increasingly used to study and forecast space weather phenomena (e.g., see review by Camporeale, 2019). Often this has taken the form of forecasting a geomagnetic index (Liemohn et al., 2018), for example, the Sym‐H (Bhaskar & Vichare, 2019; Siciliano et al., 2020), Dst/Est (Chandorkar et al., 2017; Gruet et al., 2018; Kugblenu et al., 1999; Lethy et al., 2018; Lundstedt et al., 2002; Tasistro‐Hart et al., 2021; Wintoft & Wik, 2018; Wu & Lundstedt, 1996) or Kp indices (Ji et al., 2013; Tan et al., 2018; Wing et al., 2005; Wintoft et al., 2017; Zhelavskaya et al., 2019). Models have also been produced that aim to predict phenomena such as ionospheric current systems (Kunduri et al., 2020), geomagnetic storms (Chakraborty & Morley, 2020), substorms (Maimaiti et al., 2019) or SSCs (Smith, Rae, Forsyth, Oliveira, et al., 2020).…”
Section: Introductionmentioning
confidence: 99%