Forecasting Earthquake Impact Scenarios in Istanbul with Machine Learning Algorithms

Authors : Remzi Gürfidan, Mehmet Ali Yalçınkaya

Pages : 95-105

View : 46 | Download : 39

Publication Date : 2024-11-25

Article Type : Research Paper

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.
Keywords : Deprem etki tahmini, afet hazırlığı, makine öğrenmesi, kentsel risk yönetimi