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

Year 2024, , 1334 - 1349, 03.12.2024

Anıl Utku

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

Abstract

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

Keywords

Deprem derin öğrenme makine öğrenmesi cnn lstm

References

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  • 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

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

Year 2024, , 1334 - 1349, 03.12.2024

Anıl Utku

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

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

Earthquake deep learning machine learning cnn lstm

References

  • 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
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Deep Learning
Journal Section Computer Engineering
Authors

Anıl Utku 0000-0002-7240-8713

Publication Date December 3, 2024
Submission Date April 9, 2024
Acceptance Date May 23, 2024
Published in Issue Year 2024

Cite

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