Evaluating the Components of Social and Economic Resilience against earthquake in the 3rd Municipal District of Shiraz City Using Artificial Neural Network

Today, local communities are struggling to find conditions that will allow them to return quickly to the pre-crisis situation in the event of a crisis. In recent years, emphasis has been placed on the issue of resilience rather than vulnerability. resilience is the ability of a system to absorb perturbation, or the magnitude of disturbance that can be absorbed before a system changes its structure by changing the variables. Shiraz is located in the Zagros seismic zone with high seismicity. Considering the importance of existing land uses in the 3rd municipal district of Shiraz city, the aim of this study was an evaluation of social and economic resilience in this district. This applied research is using descriptive and analytical methods. The indicators of social and economic resilience were identified from the literature, and then data were collected through a field study using questionnaires. Data were analysed using multiple linear regression and feedforward multilayer perceptron artificial neural network. Linear regression indicated that a decrease in share of income spent on necessities could result in an increase in social and economic resilience of the households under study. Neural network analysis revealed that social capital and employment recovery are the most and least effective factors. In the population under study, social component, was the most important determinant of resilience.