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Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques

Cilt: 10 Sayı: 2 24 Aralık 2025
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Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques

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Earthquakes are among the most upsetting natural events, capable of causing enormous destruction and loss of human lives. Understanding and mitigating their impacts requires sophisticated analysis tools. One viable strategy entails applying business intelligence (BI), which can effectively examine extensive historical earthquake records alongside with real-time seismic data. By exploring patterns hidden within this information, researchers can potentially uncover early signals indicating an impending earthquake. In this paper, the aim is to apply BI techniques to comprehensivly analyse earthquake data collected between 1843 and April 2025, focusing on significant seismic events with magnitudes greater than five on the Richter scale. To achieve this, a comprehensive seismic data warehouse was established to enable systematic analysis of past earthquake occurrences. Through the request for data mining methods, this research investigates the complex relationships between geological features and tectonic movements. These methods include clustering techniques that group similar earthquakes to help classify potential precursors to larger seismic events. Additionally, classification approaches categorize earthquakes by their severity low-risk, medium-risk, and high-risk while regression analysis forecasts specific earthquake features such as magnitude, depth, and geographical location. Moreover, the research employes time-series analytical method to forecast future occurrences of significant earthquakes worldwide. Such predictive insights are vital not only for understanding seismic activity patterns but also for improving existing forecasting systems. PowerBI is used for data analytics and visualization to facilitate the analysis of global large-scale earthquake data, reflecting their evolution, impact, and recurring locations; data from 1843 to April 2025 were compiled and examined. Ultimaly, the goal of this work is to refine and advance earthquake analysis tools, thereby enabling more accurate predictions and enhanced preparedness strategies worldwide.

Anahtar Kelimeler

Destekleyen Kurum

The author has not received any financial support for the research, authorship, or publication of this study.

Etik Beyan

The study does not require ethics committee permission or any special permission.

Kaynakça

  1. Galkina, A., & Grafeeva, N. (2019). Machine learning methods for earthquake prediction: a survey. In: Proceedings of the Fourth Conference on Software Engineering and Information Management, pp. 25-32. Saint Petersburg, Russia.
  2. Gürsoy, G., Varol, A., & Nasab, A. (2023, May 11-12). Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review [Conference presentation]. The 11th International Symposium on Digital Forensics and Security (ISDFS), Chattanooga, TN, USA. https://doi.org/10.1109/ISDFS58141.2023.10131766
  3. Xiong, P., Tong, L., Zhang, K., Shen, X., Battiston, R., Ouzounov, D., Iuppa, R., Crookes, D., Long, C., & Zhou, H. (2021). Towards advancing the earthquake forecasting by machine learning of satellite data. Science of the Total Environment, 771, 145256. https://doi.org/10.1016/j.scitotenv.2021.145256
  4. Banna, M. H. A., Taher, K. A., Kaiser, M. S., Mahmud, M., Rahman, M. S., Sanwar Hosen, A. S. M., & Cho, G. H. (2020). Application of artificial ıntelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access, 8, 192880-192923. https://doi.org/10.1109/ACCESS.2020.3029859
  5. Nandwani, D. T., & Buradkar, V. (2022). Earthquake damage prediction using machine learning. International Journal of Creative Research Thoughts, 10(7), 206-211.
  6. Saleem, A. K., & Rashid, A. (2023). Applications of machine learning for earthquake prediction: A review. In AIP Conference Proceedings: Al-Kadhum 2nd International Conference on Modern Applications of Information and Communication Technology (MAICT), Baghdad, Iraq. 2591(1), 030042. https://doi.org/10.1063/5.0119623
  7. Alnoukari, M., Alhawasli, H. A., Abd Alnafea, H., & Zamreek, A. J. (2012). Business Intelligence: Body of Knowledge. In: El Sheikh, A., Alnoukari, M. (eds.) Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Application, IGI Global, USA, pp. 1-13.
  8. Otari, G. V., & Kulkarni, Dr. R. V. (2012). A review of application of data mining in earthquake prediction. International Journal of Computer Science and Information Technologies (IJCSIT), 3(2), 3570-3574.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yazılım Mühendisliği (Diğer)

Bölüm

Olgu Sunumu

Yayımlanma Tarihi

24 Aralık 2025

Gönderilme Tarihi

26 Temmuz 2025

Kabul Tarihi

27 Kasım 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 10 Sayı: 2

Kaynak Göster

APA
Alnoukari, M., Abdulaziz, A., & Alakkad, J. (2025). Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques. Sinop Üniversitesi Fen Bilimleri Dergisi, 10(2), 690-708. https://doi.org/10.33484/sinopfbd.1751220
AMA
1.Alnoukari M, Abdulaziz A, Alakkad J. Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques. Sinopfbd. 2025;10(2):690-708. doi:10.33484/sinopfbd.1751220
Chicago
Alnoukari, Mouhib, Anas Abdulaziz, ve Joud Alakkad. 2025. “Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques”. Sinop Üniversitesi Fen Bilimleri Dergisi 10 (2): 690-708. https://doi.org/10.33484/sinopfbd.1751220.
EndNote
Alnoukari M, Abdulaziz A, Alakkad J (01 Aralık 2025) Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques. Sinop Üniversitesi Fen Bilimleri Dergisi 10 2 690–708.
IEEE
[1]M. Alnoukari, A. Abdulaziz, ve J. Alakkad, “Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques”, Sinopfbd, c. 10, sy 2, ss. 690–708, Ara. 2025, doi: 10.33484/sinopfbd.1751220.
ISNAD
Alnoukari, Mouhib - Abdulaziz, Anas - Alakkad, Joud. “Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques”. Sinop Üniversitesi Fen Bilimleri Dergisi 10/2 (01 Aralık 2025): 690-708. https://doi.org/10.33484/sinopfbd.1751220.
JAMA
1.Alnoukari M, Abdulaziz A, Alakkad J. Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques. Sinopfbd. 2025;10:690–708.
MLA
Alnoukari, Mouhib, vd. “Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques”. Sinop Üniversitesi Fen Bilimleri Dergisi, c. 10, sy 2, Aralık 2025, ss. 690-08, doi:10.33484/sinopfbd.1751220.
Vancouver
1.Mouhib Alnoukari, Anas Abdulaziz, Joud Alakkad. Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques. Sinopfbd. 01 Aralık 2025;10(2):690-708. doi:10.33484/sinopfbd.1751220


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