Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
This research addresses disaster preparedness for Turkey's frequent earthquakes, but it is incremental as it applies existing methods to new data.
The study tackled earthquake damage prediction in Turkey by testing machine learning models, finding that Random Forest most reliably forecasts death tolls and fatalities, with earthquake magnitude and building stability identified as key factors.
Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey. Utilizing datasets on earthquake data, infrastructural quality metrics, and contemporary socioeconomic factors, we tested various machine-learning architectures to forecast death tolls and fatalities per affected population. Our findings indicate that the Random Forest model provides the most reliable predictions. The model highlights earthquake magnitude and building stability as the primary determinants of damage. This research contributes to the reduction of fatalities in future seismic events in Turkey.