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İş Zekâsı (BI) Teknikleri Kullanılarak Dünya Çapındaki Önemli Deprem Verilerinin Analizi

Yıl 2025, Cilt: 10 Sayı: 2, 690 - 708, 24.12.2025
https://doi.org/10.33484/sinopfbd.1751220

Öz

Depremler, büyük yıkıma ve can kayıplarına yol açabilen, en sarsıcı doğal olaylar arasında yer alır. Bu etkileri anlamak ve azaltmak, gelişmiş analiz araçlarının kullanılmasını gerektirir. Uygulanabilir stratejilerden biri, kapsamlı tarihsel deprem kayıtları ile gerçek zamanlı sismik verileri birlikte inceleyebilen iş zekâsı (BI) yöntemlerinin kullanılmasıdır. Bu verilerin sunduğu gizli kalıpların incelenmesiyle, araştırmacılar yaklaşmakta olan depremlere işaret edebilecek erken sinyalleri potansiyel olarak ortaya çıkarabilir. Bu çalışmada amaç, 1843 ile Nisan 2025 yılları arasında kaydedilen ve Richter ölçeğinde beşin üzerindeki büyük sismik olaylara odaklanan deprem verilerini kapsamlı biçimde analiz etmek için BI tekniklerini uygulamaktır. Bu hedef doğrultusunda, geçmiş deprem olaylarının sistematik bir şekilde incelenmesini mümkün kılan kapsamlı bir sismik veri tabanı oluşturulmuştur. Veri madenciliği yöntemlerinden yararlanılarak jeolojik özellikler ile tektonik hareketler arasındaki karmaşık ilişkiler araştırılmıştır. Bu yöntemler arasında, benzer depremleri gruplandırarak daha büyük sismik olayların potansiyel öncüllerini sınıflandırmaya yardımcı olan kümeleme teknikleri yer almaktadır. Ayrıca, sınıflandırma yaklaşımları depremleri düşük riskli, orta riskli ve yüksek riskli olarak kategorize ederken; regresyon analizleri büyüklük, derinlik ve coğrafi konum gibi belirli deprem özelliklerini tahmin etmektedir. Bunun yanı sıra, zaman serisi analizi yöntemi dünya genelinde gelecekte meydana gelebilecek önemli depremlerin öngörülmesinde kullanılmıştır. Bu tür öngörüler, yalnızca sismik aktivite kalıplarının anlaşılması için değil, mevcut tahmin sistemlerinin iyileştirilmesi açısından da büyük önem taşımaktadır. Küresel ölçekte büyük deprem verilerinin analizi, gelişimi, etkisi ve tekrar eden konumlarını yansıtmak amacıyla Power BI kullanılmış; 1843’ten Nisan 2025’e kadar olan veriler derlenerek incelenmiştir. Sonuç olarak, bu çalışmanın amacı deprem analiz araçlarını geliştirmek ve iyileştirmek; böylece daha doğru tahminler yapılmasını ve dünya genelinde hazırlık stratejilerinin güçlendirilmesini sağlamaktır.

Kaynakça

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Worldwide Significant Earthquakes Data Analytics Using Business Intelligence Techniques

Yıl 2025, Cilt: 10 Sayı: 2, 690 - 708, 24.12.2025
https://doi.org/10.33484/sinopfbd.1751220

Öz

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.

Etik Beyan

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

Destekleyen Kurum

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

Kaynakça

  • 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.
  • 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
  • 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
  • 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
  • Nandwani, D. T., & Buradkar, V. (2022). Earthquake damage prediction using machine learning. International Journal of Creative Research Thoughts, 10(7), 206-211.
  • 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
  • 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.
  • 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.
  • McBrearty, I., & Beroza, G. (2023). Earthquake phase association with graph neural networks. arXiv:2209.07086. https://doi.org/10.1785/0120220182
  • Zhang, Y., & Gao, S. S. (2024). Using convolutional neural network to determine time window for analyzing local shear‐wave splitting measurements. Seismological Research Letters, 95(6), 3626–3632. https://doi.org/10.1785/0220230410
  • Jarah, N. B., Alasadi, A. H. H., & Hashim, K. M. (2024). A new algorithm for earthquake prediction using machine learning methods. Journal of Computer Science, 20(2), 150-156. https://doi.org/10.3844/jcssp.2024.150.156
  • Chittora, P., Chakrabarti, T., Debnath. P., Gupta, A., Chakrabarti, P., Praveen, S. P., Margala, M., & Elngar, A. A. (2022). Experimental analysis of earthquake prediction using machine learning classifiers, curve fitting, and neural modeling. Research Square. https://doi.org/10.21203/rs.3.rs-1896823/v1
  • Hoque, A., Raj, J., & Saha, Dr. A. (2018, December 14-15). Approaches of Earthquake Magnitude Prediction using machine learning techniques [Conference presentation]. International Conference on Computational Intelligence & IoT (ICCIIoT), Tripura, India. https://icciiot2018.iaasse.org/
  • Bangar, M. H., Gupta, D., Gaikwad, S., Marekar, B., & Patil, J. (2020). Earthquake prediction using machine learning algorithm, International Journal of Recent Technologies and Engineering, 8(6), 4684-4688. https://doi.org/10.35940/ijrte.E9110.018620
  • Zaidoun, A., Diko, F., & Alnoukari, M. (2008). Enhancing education quality assurance using ınformation systems- QAAS system. In IEEE International Symposium on Information Technology (ITSIM 08), Kuala Lumpur, Malaysia, pp. 1-6. https://doi.org/10.1109/ITSIM.2008.4631579.
  • Alnoukari, M., Razouk, R., & Hanano, A. (2016). BSC-SI: A framework for ıntegrating strategic ıntelligence in corporate strategic management. International Journal of Strategic Information Technology and Applications (IJSITA), 7(1), 32-44. https://doi.org/10.4018/IJSITA.2016010103
  • Lessy, D. F., Avorizano, A., & Hasan, F. N. (2022). Penerapan business ıntelligence untuk menganalisa data gempa bumi di ındonesia menggunakan tableau public. Jurnal Sistem Komputer dan Informatika (JSON), 4(2), 302. https://doi.org/10.30865/json.v4i2.5316
  • Mer, S., Saxena, S., & Pundir, A. (2024). Unveiling Earthquake Dynamics: A Comprehensive Data Analytics and LSTM-Based Prediction Model for Enhanced Seismic Forecasting. In IEEE 5th India Council International Subsections Conference (INDISCON), Chandigarh, India, pp. 1-6, https://doi.org/10.1109/INDISCON62179.2024.10744325.
  • Cremen, G., Seville, E., & Baker, J. (2020). Modeling post-earthquake business recovery time: an analytical framework. International Journal of Disaster Risk Reduction, 42, 101328. https://doi.org/10.1016/j.ijdrr.2019.101328
  • Andrade-Arenas, L., & Yactayo-Arias, C. (2024). Seismic trend analysis: a data mining approach for pattern prediction. IAES International Journal of AI, 13(3), 2623-2634. http://doi.org/10.11591/ijai.v13.i3.pp2623-2634
  • Zakeri, N. S. S., & Pashazadeh, S. (2015). Data Mining Techniques on Earthquake Data: Recent Data Mining Approaches. In M. Usman (Ed.), Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 183-199). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-4666-8513-0.ch010
  • Pasadana, I. A., Mawengkang, H., & Efendi, S. (2023). Predictive analysis of spatial data and time series to predict earthquake magnitudes by using data mining approach. Randwick International of Social Science Journal, 4(1), 80–90. https://doi.org/10.47175/rissj.v4i1.607
  • Diab-Montero, H. A., Stordal, A. S., van Leeuwen, P. J., & Vossepoel, F. C. (2025). Ensemble kalman, adaptive gaussian mixture, and particle flow filters for optimized earthquake forecasting. Computers & Geosciences, 196, 105836. https://doi.org/10.1016/j.cageo.2024.105836
  • Özcan, M., & Peker, S. (2021). Designing a data warehouse for earthquake risk assessment of buildings: a case study for healthcare facilities. Sakarya University Journal of Computer and Information Sciences, 4(1), 156-165. https://doi.org/10.35377/saucis.04.01.872729
  • Matai, P., & Bhatia, A. (2024). Architecting for real - time analytics: Leveraging stream processing and data warehousing ıntegration. International Journal of Science and Research (IJSR), 13(9), 1586-1590. https://doi.org/10.21275/sr24925170923
  • Mizutani, K., Mitarai, H., Miyazaki, K., Kumano, S., & Yamasaki, T (2024). Data-driven prediction of seismic ıntensity distributions featuring hybrid classification-regression models. arXiv:2402.02150. https://doi.org/10.48550/arXiv.2402.02150
  • Bhujang, R. K., & Kotagi, V. (2023). Earth Quack Prediction of Southern California Using Unsupervised Clustering Based Ensemble Deep Learning Model. In International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, pp. 1-6, https://doi.org/10.1109/AIKIIE60097.2023.10390335.
  • Vijaya Saraswathi, R., (2024). Machine learning-powered earthquake early warning system. International Journal of Innovative Science and Research Technology, 9(6), 1492–1503. https://doi.org/10.38124/ijisrt/IJISRT24JUN1107
  • Alabowsh, B., & Li, W. (2024, 14–19 Apr). An ınvestigation into the effectiveness of machine learning algorithms for earthquake magnitude prediction using seismic data. In EGU General Assembly, Vienna, Austria, EGU24-19288. https://doi.org/10.5194/egusphere-egu24-19288
  • Xie, Y. (2024). Deep learning in earthquake engineering: A comprehensive review. arXiv:2405.09021, https://doi.org/10.48550/arXiv.2405.09021
  • Akarsu, O. N., Akarsu, O., & Aydin, A. (2024). A bibliometric review of earthquake and machine learning research. Civil Engineering Beyond Limits, 1908, 1-10. https://doi.org/10.36937/cebel.2024.1908
  • Arrowsmith, S. J., Trugman, D. T., Bergen, K. J., & Magnani, B. M. (2022, July 5). The Big Data Revolution Unlocks New Opportunities for Seismology. https://blog.smu.edu/dedmancollege/2022/07/05/the-big-data-revolution-unlocks-new-opportunities-for-seismology/
  • Trugman, D. T., Fang, L., Ajo-Franklin, J. B., Nayak, A., & Li, Z. (2022). Preface to the focus section on big data problems in seismology. Seismological Research Letters, 93(5), 2423–2425. https://doi.org/10.1785/0220220219
  • Udegbe, E., Morgan, E., & Srinivasan, S. (2019). Big data analytics for seismic fracture ıdentification, using amplitude-based statistics. Computer Geoscience, 23, 1277–1291. https://doi.org/10.1007/s10596-019-09890-z
  • Bhattacharjee, S., Rahim, L. B. A., Ramadhani, A. W., Midhunchakkravarthy & Midhunchakkravarthy, D. (2020). A study on seismic big data handling at seismic exploration industry. In: Peng, S. L., Son, L. H., Suseendran, G., Balaganesh, D. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems. vol. 118. pp. 421-429. https://doi.org/10.1007/978-981-15-3284-9_46
  • Marketos, G., Theodoridis, Y., & Kalogeras, I. S. (2008). Seismological data warehousing and mining: A survey. International Journal of Data Warehousing and Mining, 4(1), 1–16. https://doi.org/10.4018/jdwm.2008010101 Gerasimos, M., Yannis, T., & Ioannis, S. K. (2010). Seismological Data Warehousing and Mining: A Survey. In D. Taniar & L. Rusu (Eds.), Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments (pp. 22-37). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-60566-717-1.ch002
  • Cornely, P. R., & Wang, J. (2023). Advancing earthquake prediction: A comprehensive review of data science techniques. In 6th International Conference on Computing and Big Data (ICCBD), Shanghai, China, pp. 9–16. https://doi.org/10.1109/ICCBD59843.2023.10607190
  • Kollam, M., & Joshi, A. (2023). Earthquake forecasting using optimized levenberg–marquardt back-propagation neural network. WSEAS Transactions on Computers, 22, 90–97. https://doi.org/10.37394/23205.2023.22.11
  • Ye, Q., Xia, B., & Ren, Y. (2024). Towards a multimedia big data-driven approach for earthquake monitoring and forecasting early warning system. Informatica, 48(9), 53-64. https://doi.org/10.31449/inf.v48i9.5798
  • Chandana, K. S., Sandeep, U. S., Asritha, P., Mothukuri, R., & Reddy, M. J. (2024). Seismic magnitude forecasting through machine learning paradigms: A confluence of predictive models. International Journal of Innovative Science and Research Technology, 9(6), 2606–2613. https://doi.org/10.38124/ijisrt/IJISRT24JUN2025
  • Anbazhagu, U. V., Sonia, R., Grover, R. K., Banu, E. A., Jothikumar, C., & Sudhakar, M. (2024). AI and Machine Learning in Earthquake Prediction. In F. Smarandache & P. Majumder (Eds.), Advances in Computer and Electrical Engineering Book Series, pp. 1–32. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6875-6.ch001
  • Gitis, V., & Derendyaev, A. (2019). Machine learning methods for seismic hazards forecast. Geosciences, 9(7), 308. https://doi.org/10.3390/geosciences9070308
  • Arunadevi, B., Hussain, M, M., M. I., Lakshmi, R. M. M., R., & Das, K. S. (2022). Risk Prediction of Earthquakes using Machine Learning. In 3rd International Conference Electronic Systems, Signal Processing and Computing Technologies, pp. 1589–1593. https://doi.org/10.1109/ICESC54411.2022.9885674.
  • Surisetty, H. V., Gottimukkala, S. V., & Selvam, S. (2023). A Streaming Application for Real-Time Analytics of Seismic Data. 4th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India. pp. 1-8. https://doi.org/10.1109/GCAT59970.2023.10353520
  • Atsa’am, D. D., Gbaden, T., & Wario, R. (2023). A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management. Data Science and Management, 6(4), 208-213. https://doi.org/10.1016/j.dsm.2023.06.005
  • Franke, M., Martin, H., Koch, S., & Kurzhals, K (2021). Visual analysis of spatio-temporal phenomena with 1D projections. Computer Graphics Forum, 40(3), 335-347. https://doi.org/10.1111/cgf.14311
  • Kulkarni, S., Phadke, M., Sawant, A., Patel, N., & Patil, O. (2025). Advancements in seismic data collection and analysis through machine learning. Indonesian Journal of Electrical Engineering and Computer Science, 37(3), 2058-2068. https://doi.org/10.11591/ijeecs.v37.i3.
  • Zhu, W., Alvin, H., Robert, Y., Avoy, D., Mostafa, M. S., William, E., & Gregory, B. (2022). QuakeFlow: A scalable machine-learning-based earthquake monitoring workflow with cloud computing. Geophysical Journal International, 232(1), 684–693. https://doi.org/10.1093/gji/ggac355
  • Mousavi, S. M., Sheng, Y., Weiqiang, Z., & Beroza, G. (2019). STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI. In IEEE Access, vol. 7, pp. 179464-179476, https://doi.org/10.1109/ACCESS.2019.2947848.
  • Susanta, F. F., Pratama, C., Aditya, T., Khomaini, A. F., & Abdillah, H. W. K. (2019). Geovisual analytics of spatio-temporal earthquake data in Indonesia. Journal of Geospatial Information Science and Engineering, 2(2), 185-194. https://doi.org/10.22146/jgise.51131
  • Ernez, D., Akinci, Y., Batmaz, M., Alinca Ayyildiz, Y. (2025). Analysis and visualization of earthquake data with POWER BI in Türkiye: an evaluation from the perspective of education and guidance. International Journal of Social Science, Innovation and Educational Technologies, 6(22), 45-63. http://dx.doi.org/10.54603/iss.225
  • Rasool, K., Hussain., M., Rasool., U., Umar, G., Rusool, J., Maryam, M. (2025). Seismic data analysis and earthquake prediction with IoT sensors and SmartGRU model. International Journal of Innovations in Science & Technology (IJIST), 7(3), 1376-1395. https://doi.org/10.33411/ijist/20257313761395
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Olgu Sunumu
Yazarlar

Mouhib Alnoukati 0000-0002-3982-2074

Anas Abdulaziz Bu kişi benim 0009-0005-2632-629X

Joud Alakkad Bu kişi benim 0009-0001-3499-220X

Gönderilme Tarihi 26 Temmuz 2025
Kabul Tarihi 27 Kasım 2025
Yayımlanma Tarihi 24 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 10 Sayı: 2

Kaynak Göster

APA Alnoukati, 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


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