用机器学习预测土耳其地震伤亡，发现震级和建筑稳定性最关键
Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
- 结合地震数据、建筑质量与社会经济因素，用随机森林建模
- 模型准确预测伤亡人数，震级与建筑稳定性影响最显著
- 适合灾害应急规划者参考，提升灾后响应效率
准确预测灾害损失对灾难应对至关重要，尤其在土耳其频繁发生地震的背景下。本文利用地震数据、基础设施质量指标及当代社会经济因素数据集，测试了多种机器学习架构，以预测死亡人数及每受影响人口的伤亡比例。研究结果表明，随机森林模型提供了最可靠的预测性能。该模型指出地震震级和建筑物稳定性是造成损失的主要决定因素。本研究有助于减少未来土耳其地震事件中的人员伤亡。
原文摘要 · Abstract (English)
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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