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Journals / Karaelmas Fen ve Mühendislik Dergisi / 2024 / Cilt: 14 Sayı: 3

Forecasting Earthquake Impact Scenarios in Istanbul with Machine Learning Algorithms

İstanbul’daki Deprem Etki Senaryolarının Makine Öğrenmesi Algoritmaları ile Tahmin Edilmesi

Remzi Gürfidan(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, YALVAÇ MESLEK YÜKSEKOKULU)0000-0002-4899-2219
Mehmet Ali Yalçınkaya(Ahi Evran Üniversitesi)0000-0002-7320-5643
Pages
95–105
Publication Date
2024-11-25
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AI Summary & Key Takeaways

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

This approach underscores the critical role of advanced analytics in enhancing urban disaster preparedness and management, providing valuable insights for policymaking and infrastructure development in earthquake-prone areas.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study employs machine learning algorithms to forecast the impacts of a potential magnitude 7.5 earthquake in Istanbul, focusing on casualty rates, hospitalization needs, and temporary shelter requirements.
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Key Findings & Data:Using a dataset compiled from the Istanbul Metropolitan Municipality Open Data Portal and the Turkish Statistical Institute, the research assesses Gradient Boosting, AdaBoost, Random Forest, and ExtraTrees algorithms.
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Conclusion & Impact:This approach underscores the critical role of advanced analytics in enhancing urban disaster preparedness and management, providing valuable insights for policymaking and infrastructure development in earthquake-prone areas.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deprem etki tahmini · afet hazırlığı · makine öğrenmesi

Abstract

This study employs machine learning algorithms to forecast the impacts of a potential magnitude 7.5 earthquake in Istanbul, focusing on casualty rates, hospitalization needs, and temporary shelter requirements. Using a dataset compiled from the Istanbul Metropolitan Municipality Open Data Portal and the Turkish Statistical Institute, the research assesses Gradient Boosting, AdaBoost, Random Forest, and ExtraTrees algorithms. Gradient Boosting emerged as the most effective model, exhibiting high accuracy and low prediction errors in determining disaster impacts. This approach underscores the critical role of advanced analytics in enhancing urban disaster preparedness and management, providing valuable insights for policymaking and infrastructure development in earthquake-prone areas.

Özet

Bu çalışmada, İstanbul’da olası bir 7.5 büyüklüğündeki depremin etkilerini, özellikle de can kaybı sayısı, hastaneye ihtiyaç duyacak kişi sayısı ve geçici barınma ihtiyacı duyacak kişi sayısını tahmin etmek için makine öğrenmesi algoritmaları kullanılmaktadır. İstanbul Büyükşehir Belediyesi Açık Veri Portalı ve Türkiye İstatistik Kurumu’ndan derlenen bir veri seti kullanılarak Gradyan Artırma (Gradient Boosting), Uyarlanabilir Artırma (AdaBoost), Rastgele Orman (Random Forest) ve Ekstra Ağaçlar (ExtraTrees) algoritmaları değerlendirilmiştir. Gradient Boosting modeli, yüksek doğruluk ve düşük tahmin hataları ile en etkili model olarak öne çıkmıştır. Bu yaklaşım, gelişmiş analitiklerin kentsel afet hazırlığı ve yönetimini geliştirme konusundaki kritik rolünü vurgulamakta ve depreme eğilimli bölgelerdeki alınacak önlemler ve altyapı gelişimi için değerli öngörüler sağlamaktadır.

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Cite This Article

Gürfidan, R., Yalçınkaya, M. A., *, M. A. Y. (2024). Forecasting Earthquake Impact Scenarios in Istanbul with Machine Learning Algorithms. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 14 Sayı: 3, 95–105. https://doi.org/10.7212/karaelmasfen.1494349

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