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Scientific Abstract Geomagnetic storms pose significant risks to technological systems on Earth. One of the ways to identify the level of a storm is from the Sym-H plot images. The fewer features used for image interpretation, the simpler and more efficient the analysis becomes. In this study, we applied Principal Component Analysis (PCA) to the Sym-H index images, initially consisting of seven statistical features. Through PCA, this study managed to reduce these features to just two principal components, capturing over 98% of the total variance in the first two components, thereby retaining essential information while simplifying the dataset. This reduction not only simplifies the visualization and interpretation of the Sym-H plot images but also retains the critical information necessary for understanding geomagnetic storm dynamics. By focusing on these two principal components, we can effectively present and analyse the essential patterns and behaviours of geomagnetic activity during storm events. The findings highlight the potential of PCA to enhance space weather forecasting and improve the resilience of technological infrastructure against solar storm impacts.
Aznilinda Zainuddin¹, Muhammad Asraf Hairuddin¹, Zatul Iffah Abd Latiff¹, Nur Dalila Khirul Ashar¹, Anwar Santoso², Mohamad Huzaimy Jusoh³, Ahmad Ihsan Mohd Yassin³.
¹Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Johor Branch, Pasir Gudang Campus, Malaysia.
²Center for Space, National Research and Innovation Agency, Bandung, Indonesia.
³School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia.
http://dx.doi.org/10.37231/myjas.2025.10.1.432
Principal Component Analysis (PCA) results of Sym-H index features.
Non-Scientist Abstract: Storms from the Sun, known as geomagnetic storms, can disrupt many technologies we rely on here on Earth. To understand how strong these storms are, scientists often look at images called Sym-H plots. Normally, these images involve several features that need to be studied, but using too many details can complicate the process. In this work, we used a method called Principal Component Analysis (PCA) to simplify the data. We started with seven different features and managed to reduce them to just two key ones, without losing important information. These two features alone explained almost all of the patterns found in the original data. This makes the storm images easier to interpret while still showing the main storm behaviours. The results show that simplifying the data in this way can help us better understand geomagnetic storms, which may also support efforts to improve space weather forecasting and protect our everyday technologies from solar impacts.
An overview of 33 years of trends in space weather research: a bibliometric analysis (1988-2021) - View Paper
An overview of 33 years of trends in space weather research: a bibliometric analysis (1988-2021) - View Paper
Scientific Abstract Space weather (SpW) is a phenomenon caused by a variety of solar events and has the potential to disrupt infrastructure systems and technology, putting them at risk. Despite SpW’s immense impact, there has been a notable absence of bibliometric analysis studies to understand the research trends, regional distribution, social structure, conceptual structure, and knowledge gaps. This review synthesized scopus documents of SpW domain from 1988 to 2021. In this study, three tools were used, such as Microsoft Excel, VOSviewer, and Harzing’s Publish or Perish for statistical analysis, graphical presentation, and citation metrics, respectively. Based on the 3,956 articles, roughly 70% of the articles were published in the last ten years, reveals a rapid growth in SpW research. The study discovered that China ranked third in publication volume, following the United States and the United Kingdom with Russian Federation following closely in fourth place. This study also presents six key findings, including the growth pattern of publications, contributions, and authorship collaboration by countries, most productive and influenced authors, co-authorship status, most influenced journals and articles, research cluster and new SpW subtopics discovered. These findings provide useful insight and aid in the advancement and progress of this field.
Zainuddin, A., Hairuddin, M. A., Iffah, Z., Latiff, A., & Anuar, N. M. (2021). An Overview of 33 Years of Trends in Space Weather Research : A Bibliometric Analysis ( 1998 ‐ 2021 ). https://doi.org/10.11591/eei.v14i1.8159
Citations by year and growth trajectory on SpW publications, 1988-2021 (n=3,956)
Non-Scientist Abstract: Space weather, caused by solar activity, can disrupt technologies like satellites, power grids, and communication systems. Despite its growing impact, there's been little research analyzing overall trends in this field. This study reviewed nearly 4,000 space weather papers from 1988 to 2021 using tools like Excel, VOSviewer, and Publish or Perish. Findings show rapid research growth—70% of articles were published in the last decade. The U.S. and U.K. lead in output, followed by China and Russia. The study highlights key trends in collaboration, influential authors, research clusters, and emerging topics, offering insights to guide future space weather research and development.
Prediction of geomagnetically induced currents in low-latitude regions using deep learning - View Paper
Prediction of geomagnetically induced currents in low-latitude regions using deep learning - View Paper
Scientific Abstract: Space weather, caused by solar storms, can disrupt Earth’s magnetic field and generate electric currents known as geomagnetically induced currents (GICs). These currents can damage power grids, pipelines, and communication systems. While GICs are commonly studied in higher-latitude countries, their effects in low-latitude regions closer to the equator are less understood. This study aims to predict GICs in three low-latitude locations: Huancayo (Peru), Addis Ababa (Ethiopia), and Guam (USA). The researchers used artificial intelligence models called LSTM and BiLSTM, which are designed to recognize time-based patterns. These models were trained on space weather data from a major solar storm on 31 March 2001, including solar wind speed, pressure, magnetic field strength, and geomagnetic activity. A key measurement used in this study was the rate of change in Earth’s magnetic field, which closely relates to GIC strength. The study found that organizing the training data into continuous blocks significantly improved the models’ accuracy by up to 66% compared to traditional methods. However, traditional methods were better for event-specific predictions. These findings show that AI can help predict space weather impacts even in areas where GIC research is limited, supporting better protection of critical infrastructure worldwide.
Zainuddin, A., Hairuddin, M. A., Iffah, Z., Latiff, A., Anuar, N. M., Benavides, I. F., Jusoh, M. H., Ihsan, A., & Yassin, M. (2024). Prediction of geomagnetically induced currents in low-latitude regions using deep learning. October. https://doi.org/10.18520/cs/v127/i6/691-700
Data used for the model development are taken from the low-latitude region with geomagnetic location between –30° and +30°.
Non-Scientist Abstract: This study introduces a model to predict geomagnetically induced currents (GICs), electric currents caused by space weather, that can affect power systems. The model focuses on three low-latitude locations: Huancayo (Peru), Addis Ababa (Ethiopia), and Guam (USA). It uses advanced types of artificial intelligence (AI) called LSTM and BiLSTM, which are good at spotting patterns over time. The model was tested using real data from a space weather event in March 2001. Results showed that a specific way of organizing the data improved prediction accuracy by up to 66%. However, a traditional testing method still worked better for checking accuracy during single events.
My Article for Public Reading
Aurora Cahaya Indah di Langit
My article titled "Aurora Cahaya Indah di Langit" being published in Dewan Kosmik.
In this piece, I explain how auroras are formed and whether they pose any harm to humans. You can read it in the latest issue of Dewan Kosmik—get your copy now!
Sains di Sebalik Fenomena Arus Teraruh Geomagnet
I'm excited to share that my article titled "Sains di Sebalik Fenomena Arus Teraruh Geomagnet" is featured in the August 2024 issue of Dewan Kosmik! In this piece, I dive into the fascinating science behind geomagnetically induced currents (GIC) and their impact on modern technology.
Don't miss it! Grab your copy now!
Ilmu Sains Cuaca Angkasa Membentuk Generasi Sains dan Jurutera
Saya sangat teruja untuk berkongsi bahawa artikel saya yang berjudul "Ilmu Sains Cuaca Angkasa Membentuk Generasi Sains dan Jurutera" diterbitkan dalam keluaran Oktober 2025 majalah Dewan Siswa! Dalam artikel ini, saya membincangkan betapa pentingnya ilmu sains cuaca angkasa dalam membentuk generasi saintis dan jurutera masa depan. Jom baca dan fahami lebih lanjut bagaimana pendidikan STEM boleh membekalkan kemahiran penting untuk menghadapi cabaran abad ke-21!
Jangan lepaskan peluang ini! Dapatkan salinan anda sekarang!
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