Abstract
The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.
Highlights
Earthquakes are among the most unpredictable and destructive natural hazards around the world and have caused extremely heavy damage to human life and possessions [1,2,3,4]
Considering the vast area and diverse environments of China’s mainland, to build an earthquake casualty prediction model with better applicability, it is helpful to propose a machine learning approach with submodels that are applied to different regions
This study evaluated the importance of 14 features that affect seismic fatality based on the random forest (RF) model
Summary
Earthquakes are among the most unpredictable and destructive natural hazards around the world and have caused extremely heavy damage to human life and possessions [1,2,3,4]. There have been nine catastrophic earthquakes with more than 200,000 casualties in the world, of which three occurred in China. Since 1949, more than 100 destructive earthquakes have occurred in 22 provinces of China, which have caused 270,000 casualties in total, thereby accounting for 54% of all deaths from natural disasters in this country [5]. An early prediction of the death toll that is caused by the earthquake is an essential reference for the government to determine which grade of emergency response [9] to be launched and what amount of relief supplies to be mobilized to the affected areas [10]. Rapid and accurate prediction of the number of earthquake casualties is a focus of disaster assessment research
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