DEPREM SEVİYE SINIFLANDIRMASI İÇİN HİBRİT BİR CONVLSTM MODELİ: KARŞILAŞTIRMALI BİR ANALİZ

Anıl Utku

doi:10.17780/ksujes.1467269

EN TR

A HYBRID CONVLSTM MODEL FOR EARTHQUAKE LEVEL CLASSIFICATION: A COMPARATIVE ANALYSIS

Abstract

An earthquake is a sudden shaking of the earth's surface as a result of the release of energy stored in the earth's crust. Earthquakes usually occur due to sudden breaking of underground rocks and rapid movement along a fault. In an environment where buildings and infrastructure are not properly constructed and the population is not prepared, an earthquake of even moderate intensity can be devastating. Artificial intelligence methods play an important role in predicting natural disasters, such as earthquake prediction. The hybrid ConvLSTM model developed for this purpose aimed to predict earthquake probabilities by analyzing complex energy dynamics and movements in the earth's crust from large amounts of geological data. ConvLSTM was compared with popular methods such as LR, RF, SVM, XGBoost, MLP, CNN and LSTM using real-time earthquake data provided by USGS. Experimental results showed that ConvLSTM outperformed the compared models with 0.9951 accuracy and 0.9993 AUC.

Keywords

DEPREM SEVİYE SINIFLANDIRMASI İÇİN HİBRİT BİR CONVLSTM MODELİ: KARŞILAŞTIRMALI BİR ANALİZ

Öz

Deprem, yer kabuğunda depolanan enerjinin açığa çıkması sonucu yer yüzeyinin aniden sarsılmasıdır. Depremler genellikle yer altı kayalarının aniden kırılması ve bir fay boyunca hızlı etmesi nedeniyle meydana gelir. Binaların ve altyapının düzgün inşa edilmediği ve nüfusun hazırlıklı olmadığı bir ortamda, orta şiddette bile olsa bir deprem yıkıcı olabilir. Yapay zekâ yöntemleri, deprem tahmini gibi doğal afetlerin öngörülmesinde önemli bir rol oynamaktadır. Bu amaçla geliştirilen hibrit ConvLSTM modeli ile yer kabuğundaki karmaşık enerji dinamikleri ve hareketleri, büyük miktardaki jeolojik verilerden analiz edilerek deprem olasılıklarının tahmin edilmesi amaçlandı. ConvLSTM, LR, RF, SVM, XGBoost, MLP, CNN ve LSTM gibi popüler yöntemlerle USGS tarafından sunulan gerçek zamanlı deprem verileri kullanılarak karşılaştırıldı. Deneysel sonuçlar, ConvLSTM’in 0,9951 doğruluk ve 0,9993 AUC ile karşılaştırılan modellerden daha başarılı olduğunu göstermiştir

Anahtar Kelimeler

Kaynakça

Abebe, E., Kebede, H., Kevin, M., & Demissie, Z. (2023). Earthquakes magnitude prediction using deep learning for the Horn of Africa. Soil Dynamics and Earthquake Engineering, 170, 107913. https://doi.org/10.1016/j.soildyn.2023.107913
Abri, R., & Artuner, H. (2022). LSTM-based deep learning methods for prediction of earthquakes using ionospheric data. Gazi University Journal of Science, 35(4), 1417-1431. https://doi.org/10.35378/gujs.950387
Ahire, P., Lad, H., Parekh, S., & Kabrawala, S. (2021). LSTM based stock price prediction. International Journal of Creative Research Thoughts, 9(2), 5118-5122. https://doi.org/10.6084/m9.doi.one.IJCRT2102617
Ali, Z. A., Abduljabbar, Z. H., Taher, H. A., Sallow, A. B., & Almufti, S. M. (2023). Exploring the power of eXtreme gradient boosting algorithm in machine learning: A review. Academic Journal of Nawroz University, 12(2), 320-334. https://doi.org/10.25007/ajnu.v12n2a1612
Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., & Muneer, A. (2023). LSTM inefficiency in long-term dependencies regression problems. Journal of Advanced Research in Applied Sciences and Engineering Technology, 30(3), 16-31. https://doi.org/10.37934/araset.30.3.1631
Amjad, M., Ahmad, I., Ahmad, M., Wróblewski, P., Kamiński, P., & Amjad, U. (2022). Prediction of pile bearing capacity using XGBoost algorithm: modeling and performance evaluation. Applied Sciences, 12(4), 2126. https://doi.org/10.3390/app12042126
Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2023). Logistic regression. In Multivariate Analysis: An Application-Oriented Introduction. Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-32589-3
Bytchkov, S. (2024). Seismology in the Light of Fundamental Sciences. Open Journal of Earthquake Research, 13(1), 84-112. https://doi.org/10.4236/ojer.2024.131004

Cao, J., Li, G., Shen, J., & Dai, C. (2024). IFBCLNet: Spatio-temporal frequency feature extraction-based MI-EEG classification convolutional network. Biomedical Signal Processing and Control, 92, 106092. https://doi.org/10.1016/j.bspc.2024.106092
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. https://doi.org/10.1016/j.neucom.2019.10.118
Chandra, M. A., & Bedi, S. S. (2021). Survey on SVM and their application in image classification. International Journal of Information Technology, 13(5), 1-11.
Chaudhary, M. T., & Piracha, A. (2021). Natural disasters—origins, impacts, management. Encyclopedia, 1(4), 1101-1131. https://doi.org/10.3390/encyclopedia1040084
Cinar, A. C. (2020). Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm. Arabian Journal for Science and Engineering, 45(12), 10915-10938. https://doi.org/10.1007/s13369-020-04872-1
Colombelli, S., Carotenuto, F., Elia, L., & Zollo, A. (2020). Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): Application to the PRESTo EEWS in southern Italy. Natural Hazards and Earth System Sciences, 20(4), 921-931. https://doi.org/10.5194/nhess-20-921-2020
Giridhar, U. S., Prajapati, N., & Sonkusare, R. (2021). Analysis and determination of magnitude of earthquake using sta-lta algorithm. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-5. https://doi.org/10.1109/ICCCNT51525.2021.9579939
Gomila, R. (2021). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. Journal of Experimental Psychology: General, 150(4), 700. https://doi.org/10.1037/xge0000920
He, S., Chen, T., Vennes, I., He, X., Song, D., Chen, J., & Mitri, H. (2020). Dynamic modelling of seismic wave propagation due to a remote seismic source: a case study. Rock Mechanics and Rock Engineering, 1-25. https://doi.org/ 10.1007/s00603-020-02217-w
Huang, C. J., Chen, H. Y., Chu, C. R., Lin, C. R., Yen, L. C., Yin, H. Y., & Kuo, B. Y. (2022). Low-Frequency Ground Vibrations Generated by Debris Flows Detected by a Lab-Fabricated Seismometer. Sensors, 22(23), 9310. https://doi.org/10.3390/s22239310
Kaggle. USGS Earthquakes Dataset. (2024) https://www.kaggle.com/datasets/rupindersinghrana/usgs-earthquakes-2024 Accessed 15.03.24
Kavianpour, P., Kavianpour, M., Jahani, E., & Ramezani, A. (2023). A CNN-BiLSTM model with attention mechanism for earthquake prediction. The Journal of Supercomputing, 79(17), 19194-19226. https://doi.org/10.1007/s11227-023-05369-y
Kim, H. S., Choi, D., Yoo, D. G., & Kim, K. P. (2022). Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks. Sustainability, 14(21), 13788. https://doi.org/10.3390/su142113788
Landi, F., Baraldi, L., Cornia, M., & Cucchiara, R. (2021). Working memory connections for LSTM. Neural Networks, 144, 334-341. https://doi.org/10.1016/j.neunet.2021.08.030
Li, Y., Zeng, H., Zhang, M., Wu, B., Zhao, Y., Yao, X., & Wu, F. (2023). A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering. International Journal of Applied Earth Observation and Geoinformation, 118, 103269. https://doi.org/10.1016/j.jag.2023.103269
Muhammad, D., Ahmad, I., Khalil, M. I., Khalil, W., & Ahmad, M. O. (2023). A generalized deep learning approach to seismic activity prediction. Applied Sciences, 13(3), 1598. https://doi.org/10.3390/app13031598
Nievas, C. I., Bommer, J. J., Crowley, H., van Elk, J., Ntinalexis, M., & Sangirardi, M. (2020). A database of damaging small-to-medium magnitude earthquakes. Journal of Seismology, 24(2), 263-292. https://doi.org/10.1007/s10950-019-09897-0
Ommi, S., & Hashemi, M. (2024). Machine learning technique in the north zagros earthquake prediction. Applied Computing and Geosciences, 22, 100163. https://doi.org/10.1016/j.acags.2024.100163
Pribadi, K. S., Abduh, M., Wirahadikusumah, R. D., Hanifa, N. R., Irsyam, M., Kusumaningrum, P., & Puri, E. (2021). Learning from past earthquake disasters: The need for knowledge management system to enhance infrastructure resilience in Indonesia. International Journal of Disaster Risk Reduction, 64, 102424. https://doi.org/10.1016/j.ijdrr.2021.102424
Sadhukhan, B., Chakraborty, S., & Mukherjee, S. (2022). Investigating the relationship between earthquake occurrences and climate change using RNN-based deep learning approach. Arabian Journal of Geosciences, 15(1), 31. https://doi.org/10.1007/s12517-021-09229-y
Sadhukhan, B., Chakraborty, S., & Mukherjee, S. (2023). Predicting the magnitude of an impending earthquake using deep learning techniques. Earth Science Informatics, 16(1), 803-823. https://doi.org/10.1007/s12145-022-00916-2
Shafapourtehrany, M., Batur, M., Shabani, F., Pradhan, B., Kalantar, B., & Özener, H. (2023). A comprehensive review of geospatial technology applications in earthquake preparedness, emergency management, and damage assessment. Remote Sensing, 15(7), 1939. https://doi.org/10.3390/rs15071939
Singh, P., Raj, P., & Namboodiri, V. P. (2020). EDS pooling layer. Image and Vision Computing, 98, 103923. https://doi.org/10.1016/j.imavis.2020.103923
Utku, A., & Akcayol, M. A. (2024). Hybrid Deep Learning Model for Earthquake Time Prediction. Gazi University Journal of Science, 27(3). https://doi.org/10.35378/gujs.1364529
Utku, A. (2023). Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries. Expert Systems with Applications, 231, 120769. https://doi.org/10.1016/j.eswa.2023.120769
Zakka, L., Wuyep, L. C., Monday, I. A., Kadiri, U. A., Thomas, H. Y., Ogugua, E. P., & Gambo, S. (2024). Earthquake Dynamics in Nigeria: Insights, Challenges, and Preparedness Measures. Asian Journal of Geological Research, 7(1), 58-73.
Zhou, S., & Mentch, L. (2023). Trees, forests, chickens, and eggs: when and why to prune trees in a random forest. Statistical Analysis and Data Mining: The ASA Data Science Journal, 16(1), 45-64. https://doi.org/10.1002/sam.11594
Zhou, H., Che, A., Shuai, X., & Cao, Y. (2024). Seismic vulnerability assessment model of civil structure using machine learning algorithms: a case study of the 2014 Ms6. 5 Ludian earthquake. Natural Hazards, 1-28. https://doi.org/10.1007/s11069-024-06465-9

Ayrıntılar

Birincil Dil

Türkçe

Konular

Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Anıl Utku ^*
0000-0002-7240-8713
Türkiye

Yayımlanma Tarihi

3 Aralık 2024

Gönderilme Tarihi

9 Nisan 2024

Kabul Tarihi

23 Mayıs 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1467269

IZ

https://izlik.org/JA55TU23CH

Kaynak Göster

RIS / Bibtex

APA

Utku, A. (2024). DEPREM SEVİYE SINIFLANDIRMASI İÇİN HİBRİT BİR CONVLSTM MODELİ: KARŞILAŞTIRMALI BİR ANALİZ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1334-1349. https://doi.org/10.17780/ksujes.1467269

Cited By

Deep Spatiotemporal Learning for Multivariate Water Quality Prediction: Temporal Dynamics–Aware CNN–GRU Hybrid Model

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

https://doi.org/10.46572/naturengs.1836097