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Yapay Sinir Ağı ile Entegre Farklı Sezgisel Yöntemlerin Karşılaştırılması: Deprem Hasar Tahmini için bir Vaka Çalışması

Year 2022, , 265 - 281, 31.12.2022
https://doi.org/10.26650/acin.1146097

Abstract

Depremler, tahmin edilmesi en zor doğa olayları arasında yer almaktadır. Bu öngörülemeyen deprem-lerin ardından çoğu zaman can ve mal kayıpları meydana gelmektedir. Depremler önceden kesin olarak belirlenemese bile deprem bilimciler tarafından olası konumları ve büyüklükleri yaklaşık olarak tahmin edilebilmektedir. Ancak, bu depremlerin zamanı ve bırakacağı etkinin boyutu bilinme-mektedir. Eğer olası depremlerin etkileri önceden tahmin edilebilirse, arama kurtarma çalışmaları sırasında ekiplerin hızlı ve doğru kararlar alması sağlanabilir ve bu sayede özellikle can kayıplarının önüne geçilebilir. Bu amaç doğrultusunda depremlerle ilgili tahmin modelleri geliştirmek günümüzde oldukça yaygın ve hayati bir konudur. Bu çalışmada ise dünya genelinde gerçekleşmiş yerel büyük-lüğü Ml≥3 olan açık kaynaklı deprem verileri kullanılarak farklı Makine Öğrenmesi algoritmaları karşılaştırılmış ve en yüksek performansa sahip olan algoritma seçilerek çeşitli algoritmalar ile opti-mize edilmiştir. Modellerin performansı doğruluk, Ortalama Kare Hata, Kök-Ortalama Kare Hata, kesinlik, geri çağırma ve f1 puanı gibi farklı performans değerlendirme metrikleri kullanılarak karşı-laştırılmıştır. Sonuç olarak PSO algoritması ile optimize edilmiş ANN algoritmasının 0.82 oranında doğruluk değeri ile en başarılı sonucu ürettiği gözlemlenmiştir. Elde edilen sonuçlara bakıldığında bu modelin farklı deprem hasar tahmin çalışmalarında ve acil durum planlamasında yol gösterici olarak kullanılabileceği düşünülmektedir.

References

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Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation

Year 2022, , 265 - 281, 31.12.2022
https://doi.org/10.26650/acin.1146097

Abstract

Earthquakes are among the most challenging natural phenomena to predict. Most of these unpredictable earthquakes result in the loss of human lives and property. Seismologists can estimate the probable location and magnitude of such earthquakes. However, the actual time and extent of their impact remain unknown. If the effects of possible earthquakes can be predicted, quick and accurate decisions can be made. For this purpose, developing predictive models about earthquakes is a prevalent and vital issue in the literature. In this study, various Machine Learning (ML) algorithms were compared on a public dataset of earthquakes, which had occurred worldwide and had a local magnitude Ml ≥ 3, and the algorithm with the highest performance was selected and optimized with various other algorithms. The performances of the models were compared using different performance evaluation metrics such as accuracy, Mean Square Error, Root-Mean Square Error, precision, recall, and f1 score. As a result, it was observed that the Artificial Neural Network (ANN) algorithm optimized with the Particle Swarm Optimization (PSO) algorithm produced the most successful result with an accuracy value of 0.82. Based on the obtained results, it is believed that this model can be used in different earthquake damage prediction studies and as a guide in emergency planning. 

References

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  • Gul M. & Guneri A. F. (2016). An artificial neural network-based earthquake casualty estimation model for Istanbul city. Natural Hazards, 84:2163-2178. https://doi.org/10.1007/s11069-016-2541-4 google scholar
  • Hadid B., Duviella E. & Lecoeuche S. (2020). Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification, Journal of Process Control, 86:44-56. https://doi.org/10.1016/j.jprocont.2019.12.007 google scholar
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  • Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve?. Emergency Medicine Journal, 34(6), 357-359. google scholar
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  • Jena R.& Pradhan B. (2020). Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment, International Journal of Disaster Risk Reduction, 50, ID: 101723. https://doi.org/10.1016/j.ijdrr.2020.101723 google scholar
  • Joshi JC, Kaur P, Kumar B, Singh A, Satyawali PK (2021). HIM STRAT: a neural network based model for snow cover simulation and avalanche hazard prediction over North West Himalaya, Natural Hazards, 103:1239-1260. https://doi.org/10.1007/s11069-020-04032-6 google scholar
  • Kandilli Observatory and Earthquake Research Institute, http://www.koeri.boun.edu.tr/bilgi/buyukluk.htm (16.06.2021) google scholar
  • Kaur, P., Joshi, J. C. & Aggarwal, P. (2022). A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya. Nat Hazards 110, 563–585. https://doi.org/10.1007/s11069-021-04958-5 google scholar
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  • Mcmahan H. B.& Streeter M. (2014). Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning. Advances in Neural Information Processing Systems, 1-9. google scholar Mirjalili S. (2019). Evolutionary Algorithms and Neural Networks Theory and Applications, Springer. 43-53 google scholar
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  • Moustra, M., Avraamides, M., & Christodoulou, C. (2011). Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert systems with applications, 38(12), 15032-15039.281 google scholar
  • National Geophysical Data Center / World Data Service: NCEI/WDS Global Significant Earthquake Database. NOAA National Centers for Environmental Information. https://www.ngdc.noaa.gov/hazel/view/hazards/earthquake/search (19.06.2021) google scholar
  • Obasi A. A., Ogbu K. N., Orakwe C. L. & Ahaneku I. E. (2020). Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN). Journal Of Water And Land Development, 44(I-II):98-105. 10.24425/jwld.2019.127050 google scholar
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There are 57 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Ayşe Berika Varol Malkoçoğlu 0000-0003-1856-9636

Zeynep Orman 0000-0002-0205-4198

Rüya Şamlı 0000-0002-8723-1228

Publication Date December 31, 2022
Submission Date July 22, 2022
Published in Issue Year 2022

Cite

APA Varol Malkoçoğlu, A. B., Orman, Z., & Şamlı, R. (2022). Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. Acta Infologica, 6(2), 265-281. https://doi.org/10.26650/acin.1146097
AMA Varol Malkoçoğlu AB, Orman Z, Şamlı R. Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. ACIN. December 2022;6(2):265-281. doi:10.26650/acin.1146097
Chicago Varol Malkoçoğlu, Ayşe Berika, Zeynep Orman, and Rüya Şamlı. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica 6, no. 2 (December 2022): 265-81. https://doi.org/10.26650/acin.1146097.
EndNote Varol Malkoçoğlu AB, Orman Z, Şamlı R (December 1, 2022) Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. Acta Infologica 6 2 265–281.
IEEE A. B. Varol Malkoçoğlu, Z. Orman, and R. Şamlı, “Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation”, ACIN, vol. 6, no. 2, pp. 265–281, 2022, doi: 10.26650/acin.1146097.
ISNAD Varol Malkoçoğlu, Ayşe Berika et al. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica 6/2 (December 2022), 265-281. https://doi.org/10.26650/acin.1146097.
JAMA Varol Malkoçoğlu AB, Orman Z, Şamlı R. Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. ACIN. 2022;6:265–281.
MLA Varol Malkoçoğlu, Ayşe Berika et al. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica, vol. 6, no. 2, 2022, pp. 265-81, doi:10.26650/acin.1146097.
Vancouver Varol Malkoçoğlu AB, Orman Z, Şamlı R. Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. ACIN. 2022;6(2):265-81.