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

Yıl 2022, Cilt: 6 Sayı: 2, 265 - 281, 31.12.2022

Ayşe Berika Varol Malkoçoğlu Zeynep Orman Rüya Şamlı

https://doi.org/10.26650/acin.1146097

Öz

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.

Anahtar Kelimeler

Deprem Hasar tahmini Makine öğrenmesi Optimizasyon algoritmaları Yapay sinir ağları Parçacık sürüsü optimizasyonu

Kaynakça

  • Abraham A., Rohini V. (2019). A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan. In Emerging Research in Computing, Information, Communication and Applications, 882:435-441. google scholar
  • Adjei C., Tian W., Onzo B., Kedjanyi E. A. G. & Darteh O. F., Chen S (2021). Rainfall Forecasting in Sub-Sahara Africa-Ghana using LSTM Deep Learning Approach. International Journal of Engineering Research & Technology, 10(3):464-470 google scholar
  • Aghamohammadi H., Mesgari M. S., Mansourian A. & Molaei D. (2013). Seismic human loss estimation for an earthquake disaster using neural network. International Journal of Environmental Science and Technology, 10:931-939. https://doi.org/10.1007/s13762-013-0281-5 google scholar
  • Ahmad N. (2019). Fragility Functions and Loss Curves for Deficient and Haunch-Strengthened RC Frames. Journal of Earthquake Engineering, accepted. https://doi.org/10.1080/13632469.2019.1698478 google scholar
  • Ahmad N., Ali Q., Ashraf M., Alam B. & Naem A. (2012). Seismic vulnerability of the Himalayan half-dressed rubble stone masonry structures, experimental and analytical studies, Natural Hazards and Earth System Sciences, 12:3441-3454. https://doi.org/10.5194/nhess-12-3441-2012 google scholar
  • Ahmad N., Ali Q., Crowley H. & Pinho R. (2014). Earthquake loss estimation of residential buildings in Pakistan, Natural Hazards, 73: 1889-1955. https:// doi.org/10.1007/s11069-014-1174-8 google scholar
  • Alizadeh M., Alizadeh E., Kotenaee S. A., Shahabi H., Pour A. B., Panahi M., Ahmad B. B. & Saro L. (2018). Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran, Sustainability, 10, ID: 3376. https://doi.org/10.3390/su10103376 google scholar
  • Alizadeh M., Ngah I., Hashim M., Pradhan B. & Pour A. B. (2018). A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment, Remote Sensing, 10, ID: 975. https://doi.org/10.3390/rs10060975 google scholar
  • Anupam S. and Pani P. (2020). Flood forecasting using a hybrid extreme learning machine particle swarm optimization algorithm (ELM PSO) model, Modeling Earth Systems and Environment, 6:341-347. https://doi.org/10.1007/s40808-019-00682-z280 google scholar
  • Asim K. M., Idris A., Iqbal. T & Martínez-Álvarez F. (2018). Seismic indicators-based earthquake predictor system using Genetic Programming and AdaBoost classification. Soil Dynamics and Earthquake Engineering, 111:1-7. https://doi.org/10.1016/j.soildyn.2018.04.020 google scholar
  • Bano P., Singh R. &Aggarwal G. (2021). Forecasting of Flood in Upper Yamuna Basin by Using Artificial Neural Network and Geoinformatics Techniques & Learning, Elementary Education Online, 20(5):3008-3021. 10.17051/ilkonline.2021.05.326 google scholar
  • Bath, M., (1979). Seismic risk in Fennoscandia, Tectonophysics, 57, 285–295. google scholar
  • Berry D. (1996). Statistics-A Bayesian Perspective, Duxbury Press. google scholar
  • Botton L. (2010) Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of 19th International Conference on Computational Statistics. 177-186 google scholar
  • Boutkhamouine B., Roux H. & Pérés F. (2020). Data-driven model for river flood forecasting based on a Bayesian network approach, Journal of Contingencies and Crisis Management, 29(3):215-227. https://doi.org/10.1111/1468-5973.12316 google scholar
  • Chawla M. & Singh A. (2021). A data efficient machine learning model for autonomous operational avalanche forecasting, Natural Hazards and Earth System Sciences, 106. https://doi.org/10.5194/nhess-2021-106 google scholar
  • Choubin B., Borji M., Hosseini F. S., Mosavi A. & Dineva A. A. (2020). Mass wasting susceptibility assessment of snow avalanches using machine learning models, Nature Research, 10, ID: 18363. https://doi.org/10.1038/s41598-020-75476-w google scholar
  • Cui S., Yin Y., Wang D., Li Z. & Wang Y. (2021). A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing, 101. https://doi.org/10.1016/j.asoc.2020.107038 google scholar
  • Dazzi S., Vacondio R. & Mignosa P. (2021). Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy), Water, 13:1612-1633. https://doi.org/10.3390/w13121612 google scholar
  • Dhunny A. Z., Seebocus R. H., Allam Z., Chuttur M. Y., Eltahan M. & Mehta H. (2020). Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study. Knowledge Engineering and Data Science, 3(1):1-10. http://dx.doi.org/10.17977/um018v3i12020p1-10 google scholar
  • Eberhart R. C & Kennedy J. (1995). A New Optimizer Using Particle Swarm Theory, in Proceeding of Symposium on Micro Machine and Human Science. Japan: Nagoya, Piscataway, NJswarm optimization and neural network. Engineering with Computers, 32:85-97. 10.1109/MHS.1995.494215 google scholar
  • Epstein, L. and Lomnitz, C. (1966). A model for the occurrence of large earthquakes, Nature, 211, 954–956. google scholar
  • Gessang O. M. & Lasminto U. (2020). The flood prediction model using Artificial Neural Network (ANN) and weather Application Programming Interface (API) as an alternative effort to flood mitigation in the Jenelata Subwatershed. In Proceedings of 4th International Conference on Civil Engineering Research. 10.1088/1757-899X/930/1/012080 google scholar
  • Goodfellow I., Bengio Y. & Courville A. (2016). Deep Learning. MIT Press, Cambridge, 96-161. google scholar
  • Gordan B., Armaghani D. J., Hajihassani M. & Monjezi M. (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers 32:85-97. https://doi.org/10.1007/s00366-015-0400-7 google scholar
  • 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
  • Hardin D., Guyon I. & Aliferis C. F. (2011). A Gentle Introduction to Support Vector Machines in Biomedicine. World Scientific, 2011 google scholar
  • Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve?. Emergency Medicine Journal, 34(6), 357-359. google scholar
  • Hssina B., Merbouha A., Ezzikouri H. & Erritali M. (2014). A comparative study of decision tree ID3 and C4. 5. International Journal of Advanced Computer Science and Applications, 4(2), 13-19. google scholar
  • 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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  • Li C. & Liu X. (2016). An improved PSO-BP neural network and its application to earthquake prediction. In Proceeding of Chinese Control and Decision Conference, 3434-3438. 10.1109/CCDC.2016.7531576 google scholar
  • 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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Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation

Yıl 2022, Cilt: 6 Sayı: 2, 265 - 281, 31.12.2022

Ayşe Berika Varol Malkoçoğlu Zeynep Orman Rüya Şamlı

https://doi.org/10.26650/acin.1146097

Öz

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. 

Anahtar Kelimeler

Earthquake Damage prediction Machine learning Optimization algorithms Artificial neural networks Particle swarm optimization

Kaynakça

  • Abraham A., Rohini V. (2019). A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan. In Emerging Research in Computing, Information, Communication and Applications, 882:435-441. google scholar
  • Adjei C., Tian W., Onzo B., Kedjanyi E. A. G. & Darteh O. F., Chen S (2021). Rainfall Forecasting in Sub-Sahara Africa-Ghana using LSTM Deep Learning Approach. International Journal of Engineering Research & Technology, 10(3):464-470 google scholar
  • Aghamohammadi H., Mesgari M. S., Mansourian A. & Molaei D. (2013). Seismic human loss estimation for an earthquake disaster using neural network. International Journal of Environmental Science and Technology, 10:931-939. https://doi.org/10.1007/s13762-013-0281-5 google scholar
  • Ahmad N. (2019). Fragility Functions and Loss Curves for Deficient and Haunch-Strengthened RC Frames. Journal of Earthquake Engineering, accepted. https://doi.org/10.1080/13632469.2019.1698478 google scholar
  • Ahmad N., Ali Q., Ashraf M., Alam B. & Naem A. (2012). Seismic vulnerability of the Himalayan half-dressed rubble stone masonry structures, experimental and analytical studies, Natural Hazards and Earth System Sciences, 12:3441-3454. https://doi.org/10.5194/nhess-12-3441-2012 google scholar
  • Ahmad N., Ali Q., Crowley H. & Pinho R. (2014). Earthquake loss estimation of residential buildings in Pakistan, Natural Hazards, 73: 1889-1955. https:// doi.org/10.1007/s11069-014-1174-8 google scholar
  • Alizadeh M., Alizadeh E., Kotenaee S. A., Shahabi H., Pour A. B., Panahi M., Ahmad B. B. & Saro L. (2018). Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran, Sustainability, 10, ID: 3376. https://doi.org/10.3390/su10103376 google scholar
  • Alizadeh M., Ngah I., Hashim M., Pradhan B. & Pour A. B. (2018). A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment, Remote Sensing, 10, ID: 975. https://doi.org/10.3390/rs10060975 google scholar
  • Anupam S. and Pani P. (2020). Flood forecasting using a hybrid extreme learning machine particle swarm optimization algorithm (ELM PSO) model, Modeling Earth Systems and Environment, 6:341-347. https://doi.org/10.1007/s40808-019-00682-z280 google scholar
  • Asim K. M., Idris A., Iqbal. T & Martínez-Álvarez F. (2018). Seismic indicators-based earthquake predictor system using Genetic Programming and AdaBoost classification. Soil Dynamics and Earthquake Engineering, 111:1-7. https://doi.org/10.1016/j.soildyn.2018.04.020 google scholar
  • Bano P., Singh R. &Aggarwal G. (2021). Forecasting of Flood in Upper Yamuna Basin by Using Artificial Neural Network and Geoinformatics Techniques & Learning, Elementary Education Online, 20(5):3008-3021. 10.17051/ilkonline.2021.05.326 google scholar
  • Bath, M., (1979). Seismic risk in Fennoscandia, Tectonophysics, 57, 285–295. google scholar
  • Berry D. (1996). Statistics-A Bayesian Perspective, Duxbury Press. google scholar
  • Botton L. (2010) Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of 19th International Conference on Computational Statistics. 177-186 google scholar
  • Boutkhamouine B., Roux H. & Pérés F. (2020). Data-driven model for river flood forecasting based on a Bayesian network approach, Journal of Contingencies and Crisis Management, 29(3):215-227. https://doi.org/10.1111/1468-5973.12316 google scholar
  • Chawla M. & Singh A. (2021). A data efficient machine learning model for autonomous operational avalanche forecasting, Natural Hazards and Earth System Sciences, 106. https://doi.org/10.5194/nhess-2021-106 google scholar
  • Choubin B., Borji M., Hosseini F. S., Mosavi A. & Dineva A. A. (2020). Mass wasting susceptibility assessment of snow avalanches using machine learning models, Nature Research, 10, ID: 18363. https://doi.org/10.1038/s41598-020-75476-w google scholar
  • Cui S., Yin Y., Wang D., Li Z. & Wang Y. (2021). A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing, 101. https://doi.org/10.1016/j.asoc.2020.107038 google scholar
  • Dazzi S., Vacondio R. & Mignosa P. (2021). Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy), Water, 13:1612-1633. https://doi.org/10.3390/w13121612 google scholar
  • Dhunny A. Z., Seebocus R. H., Allam Z., Chuttur M. Y., Eltahan M. & Mehta H. (2020). Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study. Knowledge Engineering and Data Science, 3(1):1-10. http://dx.doi.org/10.17977/um018v3i12020p1-10 google scholar
  • Eberhart R. C & Kennedy J. (1995). A New Optimizer Using Particle Swarm Theory, in Proceeding of Symposium on Micro Machine and Human Science. Japan: Nagoya, Piscataway, NJswarm optimization and neural network. Engineering with Computers, 32:85-97. 10.1109/MHS.1995.494215 google scholar
  • Epstein, L. and Lomnitz, C. (1966). A model for the occurrence of large earthquakes, Nature, 211, 954–956. google scholar
  • Gessang O. M. & Lasminto U. (2020). The flood prediction model using Artificial Neural Network (ANN) and weather Application Programming Interface (API) as an alternative effort to flood mitigation in the Jenelata Subwatershed. In Proceedings of 4th International Conference on Civil Engineering Research. 10.1088/1757-899X/930/1/012080 google scholar
  • Goodfellow I., Bengio Y. & Courville A. (2016). Deep Learning. MIT Press, Cambridge, 96-161. google scholar
  • Gordan B., Armaghani D. J., Hajihassani M. & Monjezi M. (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers 32:85-97. https://doi.org/10.1007/s00366-015-0400-7 google scholar
  • 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
  • Hardin D., Guyon I. & Aliferis C. F. (2011). A Gentle Introduction to Support Vector Machines in Biomedicine. World Scientific, 2011 google scholar
  • Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve?. Emergency Medicine Journal, 34(6), 357-359. google scholar
  • Hssina B., Merbouha A., Ezzikouri H. & Erritali M. (2014). A comparative study of decision tree ID3 and C4. 5. International Journal of Advanced Computer Science and Applications, 4(2), 13-19. google scholar
  • 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
  • Kingma D. P. and Ba J. L. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations, 1-13. https://doi.org/10.48550/arXiv.1412.6980 google scholar Kleinbaum D. G. & Klein M. (2010). Logistic Regression A Self‐Learning Text, Third Edition, Springer google scholar
  • Li C. & Liu X. (2016). An improved PSO-BP neural network and its application to earthquake prediction. In Proceeding of Chinese Control and Decision Conference, 3434-3438. 10.1109/CCDC.2016.7531576 google scholar
  • 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
  • Moayedi H., Mehrabi M., Mosallanezhad M., Rashid A. S. A. & Pradhan B. (2019). Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers, 35:967-984. https://doi.org/10.1007/s00366-018-0644-0 google scholar
  • 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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Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Ayşe Berika Varol Malkoçoğlu 0000-0003-1856-9636

Zeynep Orman 0000-0002-0205-4198

Rüya Şamlı 0000-0002-8723-1228

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 22 Temmuz 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 6 Sayı: 2

Kaynak Göster

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. Aralık 2022;6(2):265-281. doi:10.26650/acin.1146097
Chicago Varol Malkoçoğlu, Ayşe Berika, Zeynep Orman, ve Rüya Şamlı. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica 6, sy. 2 (Aralık 2022): 265-81. https://doi.org/10.26650/acin.1146097.
EndNote Varol Malkoçoğlu AB, Orman Z, Şamlı R (01 Aralık 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, ve R. Şamlı, “Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation”, ACIN, c. 6, sy. 2, ss. 265–281, 2022, doi: 10.26650/acin.1146097.
ISNAD Varol Malkoçoğlu, Ayşe Berika vd. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica 6/2 (Aralık 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 vd. “Comparison of Different Heuristics Integrated With Neural Networks: A Case Study for Earthquake Damage Estimation”. Acta Infologica, c. 6, sy. 2, 2022, ss. 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.
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