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Advancing Space Weather Forecasting: A Comparative Analysis of AI Techniques for Predicting Geomagnetic Storms

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Last updated: 21 Dec 2024

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Abstract

Forecasting geomagnetic storms is crucial for  mitigating their potential impacts on technology and  infrastructure. This research explores the use of artificial  intelligence (AI) techniques, particularly linear regression, and  Long Short-Term Memory (LSTM) networks, for predicting  geomagnetic storms using the OMNI dataset. The dataset,  comprising various solar and interplanetary parameters, was  preprocessed by scaling features and removing null values. A  linear regression model achieved a Root Mean Squared Error  (RMSE) of 5.95 and an R² score of 0.77. In contrast, the LSTM  model, designed to capture temporal dependencies, significantly  outperformed linear regression with an RMSE of 1.46 and an R²  score of 0.99. These results demonstrate the potential of LSTM networks in accurately forecasting geomagnetic activity, thus  providing a valuable tool for space weather prediction and the 
protection of critical technological systems.

DOI

10.21608/iiis.2024.357835

Keywords

Geomagnetic Storms, Forecasting, NASA, Deep , learning, Machine Learning, artificial intelligence

Authors

First Name

Shaimaa

Last Name

Salah

MiddleName

-

Affiliation

Department of Artificial Intelligence Misr University For Science And Technology Cairo, Egypt

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Orcid

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First Name

Asmaa

Last Name

ElSayed

MiddleName

-

Affiliation

Department of Artificial Intelligence Misr University For Science And Technology Cairo, Egypt

Email

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City

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Orcid

-

First Name

Omar

Last Name

Khaled

MiddleName

-

Affiliation

Department of Artificial Intelligence Misr University For Science And Technology Cairo, Egypt

Email

94105@must.edu.eg

City

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Orcid

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First Name

Mohanad

Last Name

Deif

MiddleName

-

Affiliation

Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt

Email

mohanad.deif@must.edu.eg

City

-

Orcid

-

First Name

Rania

Last Name

Elgohary

MiddleName

-

Affiliation

Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt

Email

rania.elgohary@must.edu.eg

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-

Orcid

-

Volume

1

Article Issue

2

Related Issue

48104

Issue Date

2024-06-01

Receive Date

2024-06-02

Publish Date

2024-06-01

Online ISSN

2682-258X

Article File

IIIS3578351717189200.pdf

PDF . 371.6kB

Link

https://iiis.journals.ekb.eg/article_357835.html

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https://iiis.journals.ekb.eg/service?article_code=357835

Order

357,835

Publication Type

Journal

Publication Title

International Integrated Intelligent Systems

Publication Link

https://iiis.journals.ekb.eg/

MainTitle

Advancing Space Weather Forecasting: A Comparative Analysis of AI Techniques for Predicting Geomagnetic Storms

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Type

Article

Created At

21 Dec 2024