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Journals / Acta Infologica / 2022 / Volume: 6 Issue: 2

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

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

Ayşe Berika Varol Malkoçoğlu(BEYKOZ ÜNİVERSİTESİ)0000-0003-1856-9636
Zeynep Orman(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ PR.)0000-0002-0205-4198
Rüya Şamlı(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, YAZILIM MÜHENDİSLİĞİ ANABİLİM DALI)0000-0002-8723-1228
Pages
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Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Earthquakes are among the most challenging natural phenomena to predict.
📊
Key Findings & Data:If the effects of possible earthquakes can be predicted, quick and accurate decisions can be made.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Earthquake · Damage prediction · Machine learning

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.

Özet

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.

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Cite This Article

Malkoçoğlu, A. B. V., Orman, Z., Şamlı, R. (2022). Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation. Acta Infologica, Volume: 6 Issue: 2. https://doi.org/10.26650/acin.1146097

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