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

Lethy, Ahmed; Eleraki, Mohamed; Samy, Aalaa; Deebes, H.A.

doi:10.1029/2018sw001863

Cited by 25 publications

(16 citation statements)

References 50 publications

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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%

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith¹,

Rae

Forsyth³

et al. 2020

Space Weather

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In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ∼0.21 and 0.82, respectively. The most important parameter for these predictions was found to be the minimum observed B Z. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, for example, from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ∼ 0.16, ROC Scores ∼ 0.8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith¹,

Rae

Forsyth³

et al. 2020

Space Weather

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“…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%

Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms

et al. 2019

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In this study, different hybridized techniques that combine an artificial neural network (ANN) with bio‐inspired optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and a hybridized PSO+GA were applied to update the ANN connection weights and so forecast the disturbance storm time (Dst) index. The past values of Dst index time series were used as input parameters to forecast its variation from 1 to 6 hours ahead. The database collected considers 233,760 hourly data from 01 January 1990 to 31 August 2016, containing storms and quiet period, grouped into three data sets: learning set (116,880 hourly data points), validation set (58,440 data points), and testing set (58,440 data points). Several ANN configurations were studied and optimized during the training process by evaluating the root mean square error (RMSE) and the correlation coefficient (R). An analysis of the predictive capability of each method was made year per year, and according to the levels of the geomagnetic storm. Also, an additional test was applied to the proposed method using 17 intense geomagnetic storms reported during solar cycle 24, including the St. Patrick's Day storm of 2015. Results show that the hybridized ANN+PSO method can forecast the Dst index quite accurately from 1 to 3 h in advance (with RMSE≤5 nT and R≥0.9), while the ANN+PSO+GA method can forecast the Dst index quite accurately from 4 to 6 h ahead (RMSE≤7 nT and R≥0.8)

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“…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%

Forecasting the Probability of Large Rates of Change of the Geomagnetic Field in the UK: Timescales, Horizons, and Thresholds

Smith¹,

Forsyth²,

Rae

et al. 2021

Space Weather

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Large geomagnetically induced currents (GICs) pose a risk to ground based infrastructure such as power networks. Large GICs may be induced when the rate of change of the ground magnetic field is significantly elevated. We assess the ability of three different machine learning model architectures to process the time history of the incoming solar wind and provide a probabilistic forecast as to whether the rate of change of the ground magnetic field will exceed specific high thresholds at a location in the UK. The three models tested represent feed forward, convolutional and recurrent neural networks. We find all three models are reliable and skillful, with Brier skill scores, receiver‐operating characteristic scores and precision‐recall scores of approximately 0.25, 0.95 and 0.45, respectively. When evaluated during two example magnetospheric storms we find that all scores increase significantly, indicating that the models work better during active intervals. The models perform excellently through the majority of the storms, however they do not fully capture the ground response around the initial sudden commencements. We attribute this to the use of propagated solar wind data not allowing the models notice to forecast impulsive phenomenon. Increasing the volume of solar wind data provided to the models does not produce appreciable increases in model performance, possibly due to the fixed model structures and limited training data. However, increasing the horizon of the forecast from 30 min to 3 h increases the performance of the models, presumably as the models need not be as precise about timing.

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

Cited by 25 publications

References 50 publications

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms

Forecasting the Probability of Large Rates of Change of the Geomagnetic Field in the UK: Timescales, Horizons, and Thresholds

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