Automatic detection of auroral Pc5 geomagnetic pulsation using machine learning approach guided with discrete wavelet transform

Omondi, Stephen; Yoshikawa, Akimasa; Zahra, Waheed K.; Fathy, Ibrahim; Mahrous, Ayman

doi:10.1016/j.asr.2022.06.063

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…After successfully training the network, the close-loop NARX architecture was employed in forecasting GICs. This type of NARX architecture is similar to the Feed-forward neural network (FNN) described by (Omondi et al, 2022a). In this architectural state, the inputs are fed into the hidden layer.…”

Section: Narx Neural Networkmentioning

confidence: 99%

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Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Pappoe,

Tete,

Mahrous

2023

Preprint

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Geomagnetically induced currents (GICs) are low-frequency quasi direct currents caused by the complex interplay between Earth geomagnetic field and continuous high speed solar wind stream (HSS). The result of this interaction poses significant threats to the operations of technological infrastructures such as power grids. The integrity of oil pipelines may also be affected by the influx of GICs. As such, developing models for accurate and timely forecast of GICs is essential for mitigating the potential hazards and safeguarding valuable systems from the extreme effects of these currents. In this work, we propose a machine learning model based on the Nonlinear Autoregression with Exogenous inputs (NARX) for forecasting GICs. The model developed here forecasts GICs using inputs solely based on the solar and interplanetary parameters. The solar and interplanetary parameters retrieved from Omni Web during the maximum and minimum phases of solar cycle 23 were utilized. The developed model takes the solar wind speed, Bz, IMF, AE, ASYH, and SYMH as inputs and the measured GICs obtained from the Finnish Meteorological Institute (Mäntsälä) as the target. We validated the model using measured GICs during the geomagnetic storm periods of November 20 -23 2003, November 7 -13 2004 and August 24 -26, 2005. The model's performance was evaluated using the cross-correlation coefficient (R), root-mean square error (RMSE)and the wavelet coherence analysis. The prediction accuracy for each of the individual storms are 69 %, 68 %, and 70 %, respectively. For these events, RMSEs of 1.17 A, 1.58 A and 0.56 A respectively were obtained in each case indicating the robustness of the model. The approach presented augments existing works and will contribute to the forecasting of GICs in areas where the geoelectric field and local geophysical parameters are not readily available.

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Section: Narx Neural Networkmentioning

confidence: 99%

“…We utilized wavelet-based coherence analysis to study the timefrequency properties of the observed and forecast GICs. The approach employed follows the procedure described by (Omondi et al, 2022b(Omondi et al, , 2022a.…”

Section: Network Accuracy Parametersmentioning

confidence: 99%

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Pappoe,

Tete,

Mahrous

2023

Preprint

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“…This process is timeconsuming and slows down the analysis of space weather data even as the amount of available data has increased. Previous work has sought to develop methods to automate this process, including use of Fourier Transform methods to analyze the power spectral density of wave data (Bortnik et al, 2007;Kim et al, 2018;Di Matteo et al, 2021;Inglis et al, 2015;Inglis et al, 2016;Murphy et al, 2020), discrete wavelet transforms (Omondi et al, 2022), and trigger algorithms that look for cases of simultaneity between two or more variables known to correlate with the events of interest (Carson et al, 2013). Few studies have explored the use of image analysis and object identification algorithms to automatically detect wave events directly from spectrogram images (Antonopoulou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Developing a Neural Network for the Identification of EMIC Wave Events

Capman,

Simms,

Engebretson

2023

Preprint

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A supervised convolutional neural network (CNN) was developed to automatically identify electromagnetic ion cyclotron (EMIC) wave events from spectrograms. These events have usually been identified manually, which can be a time-consuming process. Statistical analyses of larger datasets would be facilitated if this process were simplified. The neural network model was trained on spectrogram images from the Halley magnetometer station that had been manually identified as either containing or not containing an EMIC wave event anywhere in the spectrogram. This model was tested on an unseen set of spectrograms, achieving a perfect true positive rate of 1. Size, time, frequency, and pixel color information was extracted from each identified event and exported into a spreadsheet for easier analysis. This method has the capability of reducing time and effort required to identify important spectrogram features by hand. Such an automated method could be applied to other space weather data stored in spectrograms.

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Geomagnetic disturbance of the meridian chain at mid and low latitudes during 2015 geomagnetic storms

Ma,

Lin,

Wang

et al. 2024

Astrophys Space Sci

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Automatic detection of auroral Pc5 geomagnetic pulsation using machine learning approach guided with discrete wavelet transform

Cited by 6 publications

References 48 publications

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Developing a Neural Network for the Identification of EMIC Wave Events

Geomagnetic disturbance of the meridian chain at mid and low latitudes during 2015 geomagnetic storms

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