A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas

Urbanization can be a challenge and an opportunity for earthquake risk mitigation. However, little is known about the changes in exposure (for example, population and urban land) to earthquakes in the context of global urbanization, and their impacts on fatalities in earthquake-prone areas. We present a global analysis of the changes in population size and urban land area in earthquake-prone areas from 1990 to 2015, and their impacts on earthquake-related fatalities. We found that more than two thirds of population growth (or 70% of total population in 2015) and nearly three quarters of earthquake-related deaths (or 307,918 deaths) in global earthquake-prone areas occurred in developing countries with an urbanization ratio (percentage of urban population to total population) between 20 and 60%. Holding other factors constant, population size was significantly and positively associated with earthquake fatalities, while the area of urban land was negatively related. The results suggest that fatalities increase for areas where the urbanization ratio is low, but after a ratio between 40 and 50% occurs, earthquake fatalities decline. This finding suggests that the resistance of building and infrastructure is greater in countries with higher urbanization ratios and highlights the need for further investigation. Our quantitative analysis is extended into the future using Shared Socioeconomic Pathways to reveal that by 2050, more than 50% of the population increase in global earthquake-prone areas will take place in a few developing countries (Pakistan, India, Afghanistan, and Bangladesh) that are particularly vulnerable to earthquakes. To reduce earthquake-induced fatalities, enhanced resilience of buildings and urban infrastructure generally in these few countries should be a priority.

The Ratio of Rural to Urban People Killed in Earthquakes Needs to be Assessed for Countries Separately, the Example of Colombia

The hypothesis that very large earthquakes kill predominantly rural people is tested for the case of Colombia. For models of the eight largest earthquakes that have occurred in Colombia, the hypothetical ratios of rural to urban fatalities are calculated. Choosing these historic ruptures does not mean that they are expected to reoccur soon; instead, they are selected to sample fatalities in realistic models. The fatalities due to the assumed earthquakes are calculated by the tool Quake Loss Alerts for Rescue and Mitigation (QLARM), using the population data from the 2018 Columbian census. The minimum population for a settlement to be classified as urban is 35,000. The results do not depend on this limit. The condition for a model calculation to be accepted is that it matches the maximum intensities of shaking reported in the literature for the earthquake in question. Of the eight hypothetical earthquakes, today four would predominantly kill rural people, three would be urban, and one is about evenly split. However, the sum of the hypothetical fatalities in the eight test earthquakes is 79% urban and only 21% rural. This means that the observation that worldwide more than 90% of earthquake fatalities are rural does not apply to Colombia. The reasons for this contrast are that on average the Colombian test earthquakes are only about half as long as the ruptures modeled in the worldwide sample. Relatively short ruptures (75 km on average in Colombia) can rip through industrial areas without many villages because Colombia is strongly industrialized. By implication, this result means that the observation that most large earthquakes kill mostly rural people cannot be applied universally. The likely ratio of rural to urban earthquake fatalities has to be determined for each country separately for the assessment of the ratio of mitigation efforts to be allocated locally.

An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

This study aims to analyze and compare the importance of feature affecting earthquake fatalities in China mainland and establish a deep learning model to assess the potential fatalities based on the selected factors. The random forest (RF) model, classification and regression tree (CART) model, and AdaBoost model were used to assess the importance of nine features and the analysis showed that the RF model was better than the other models. Furthermore, we compared the contributions of 43 different structure types to casualties based on the RF model. Finally, we proposed a model for estimating earthquake fatalities based on the seismic data from 1992 to 2017 in China mainland. These results indicate that the deep learning model produced in this study has good performance for predicting seismic fatalities. The method could be helpful to reduce casualties during emergencies and future building construction.

Predicting Fatality Rates Due to Earthquakes Accounting for Community Vulnerability

The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality data from 61 earthquakes affecting Taiwan from 1999 to 2016, as well as information on the socioeconomic and environmental characteristics of the affected communities. Using a local seismic hazard map, the calibrated prediction model is used in a seismic risk analysis for Taiwan that predicts the expected fatality rates and counts caused by earthquakes in future years.

Quantification of uncertainty in rapid estimation of earthquake fatalities based on scenario analysis

The rapid estimation of earthquake fatalities using earthquake parameters is the core basis for emergency response. However, there are numerous factors affecting earthquake fatalities, and it is impossible to obtain an accurate estimation result. The key to solve this problem is quantifying the uncertainty. In this paper, we proposed a new method to estimate earthquake fatalities and quantify the uncertainty based on basic earthquake emergency scenarios. The accuracy of the model is verified by earthquake that occurred during recent year. The preliminary analysis and comparison results show that the model is more effective and reasonable and can also provide a theoretical basis for post-earthquake emergency response.