Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis

• URL: http://arxiv.org/abs/2411.08903v1
• Date: Tue, 29 Oct 2024 10:29:06 GMT
• Title: Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
• Authors: Shrey Shah, Alex Lin, Scott Lin, Josh Patel, Michael Lam, Kevin Zhu,
• Abstract summary: This research contributes to the reduction of fatalities in future seismic events in Turkey. 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.
• Score: 1.9461727843485295
• License:
• Abstract: 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.

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