Coupled Human Activities And Seismic Disaster

With the increasingly severe occurrences of natural disasters, it conduct a research on the relationships between earthquake and human being’s activities in seismically active areas, to explore the influence of different community status and human being’s activities on community vulnerability, adaptability and resilience.Firstly, a quantitative assessment model for community resilience is proposed to analyze the variability of a society’s resilience to seismic hazards determined by the socioeconomic status, which includes three parts, the vulnerability assessment model, the adaptability assessment model and the resilience assessment model. Wherein, the definition of vulnerability, adaptability and resilience are improved, the vulnerability and adaptability measure methods are proposed, that is, the difference between the actual consequences(damage) and predicted(normal) consequences(damage) under certain exposure is defined as the indicator of a community’s vulnerability, and the change rate of the community state before and after the disaster event is used to measure adaptability.Secondly, the seismic hazard resiliency of 110 counties in the 2008 Wenchuan Earthquake area of China is studied. Thirteen key socioeconomic and demographic variables, documented by the population census data and provincial statistical yearbooks of China are used as assessment analyses. On the base of community vulnerability and adaptability measure methods, the measurable indicators of the quantitative assessment model is presented, which is applicable to the earthquake disaster, including the exposure index, the vulnerability index and the adaptability index. The quantitative assessment model for community vulnerability and adaptability is modeled to explore the effects of different social economy status on community resilience.Next, the factor analysis was used to consolidate the socioeconomic variables to four comprehensive factors. The global regression model confirmed the relationship between the selected socio-economic factors and vulnerability or adaptability. Then the Geographically Weighted Regression model has its strength in exploring geographical heterogeneity among the counties in the study area. The GWR coefficient maps revealed the spatial pattern of the association strength of the variables with vulnerability and adaptability. The K-mean cluster method is used to segment the study area to a few subregions that bear localized characteristics defined by the regression coefficients.At last, the RIM model is improved and the improved RIM model is used to classify counties into four resilience levels according to the exposure, vulnerability index, and adaptability index, and then the discriminate analysis is applied to quantify the influence of socioeconomic characteristics on the county resilience. The analysis result shows that counties located right at the epicenter have the lowest resilience, counties immediately adjacent to the east of epicenter have the highest resilience capacities, and counties far away from the epicenter have the highest resiliency. The socioeconomic variables, including percent of ethnic minority, proportion of the second industry, average worker wage, percent of population aged 15-64, per capita GDP, are identified as the most influential socio-economic characteristics on resilience.The above research results demonstrate that this research provides useful information to improve county resilience to earthquakes and support decision-making for sustainable development.