Using artificial neural networks for comparison of the 09 March 2012 intense and 08 May 2014 weak storms

Köklü, Kevser

doi:10.1016/j.asr.2022.07.067

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Te GS consists of three phases: Te sudden commencement, the main phase, and the recovery phase. When researchers discuss any GS, they usually work a 120-hour [10][11][12][13]46] time frame with hourly data. In this frame, the GS hour is located in the middle of the time interval.…”

Section: Datamentioning

confidence: 99%

“…Te solar wind is an electromagnetic stream of charged particles constantly emitted by the Sun, and it interacts with Earth's magnetosphere-ionosphere coupling. A GS [7][8][9][10][11][12][13][14] are disturbances in the Earth's electromagnetism caused by changes in the solar wind, which have potentially signifcant impacts on the electromagnetic stability of the atmosphere [15,16]. Te GS commences with a coronal mass ejection (CME).…”

Section: Introductionmentioning

confidence: 99%

“…Te NN [9,11,14,23,[38][39][40][41][42][43][44][45] model is an instrument that imitates the human brain and converts inputs to outputs. Te data set sent to the input layer is processed in the hidden layer and presented to the output layer as expected products.…”

Section: Introductionmentioning

confidence: 99%

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Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm

Eroglu,

Koksal

2024

Discrete Dynamics in Nature and Society

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The discussion models the IRI-2012 TEC map over a moderate geomagnetic storm period (5 days) in February 2015 and compares the yield of the models. The models are constructed with the help of cubic Bézier curves and machine learning. In a sense, the comparison of a classical and mechanical approach with a modern and computer-based one is a considerable experience for the paper. The parametric curve approach governs models of piecewise continuous Bézier cubics, while the models employ only the TEC map. The design is separated into curve components at every five-hour curvature point, and each component is handled independently. Instead of the traditional least squares method for finding control points of cubics, it utilizes the mean of every five-hour of the piecewise curves of the TEC data. Accordingly, the prediction error can be controlled at a rate that can compete with the modern network approach. In the network model, 120 hours of the solar wind parameters and the TEC map of the storm are processed. The reliability of the network model is assessed by the (R) correlation coefficient and mean square error. In modeling the TEC map with the classical approach, the mean absolute error is 0.0901% and the correlation coefficient (R) score is 99.9%. The R score of the network model is 99.6%, and the mean square error is 0.71958 (TECU) (at epoch 47). The results agree with the literature.

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