Earthquake Risk Zoning for locating Safe Areas at the time of Natural Hazards Events Using Artificial Intelligence Algorithms and GIS (Case Study: District One of Ahvaz City)

Since locating temporary housing at the time of earthquake disaster is done regardless of standard criteria by relief organizations, it may pose many complications, especially in large cities. To avoid such problems, a written action plan is needed for zoning seismic prone areas in settlements such as Ahvaz city, in terms of hazardous areas, safe areas for temporary housing after the disaster, and identifying the optimal routing to reach safe areas. In this study, the above-mentioned objectives have been studied using Fuzzy inference, Fuzzy hierarchical analysis and GIS in district “one” of Ahvaz metropolitan city. For this purpose, the most important data has been collected and used to locate high-risk as well as secure areas and also identifying the optimal routs to reach safe areas. Based on the results, Fuzzy Inference System shows 85% whereas, the Fuzzy hierarchical analysis exhibits 41% agreement with expert’s choice. This results showed the higher capability of fuzzy inference system as compared to hierarchical analysis. To identify safe locations for temporary housing post to earthquake disaster, after reviewing the type and land-use of the proposed areas using two models, in Fuzzy Inference System Model, 25 sites and in the Fuzzy hierarchical analysis, 18 sites of temporary housing were determined. In terms of optimal routing, the results showed that, both Genetic and Dijkstra algorithm are appropriate and applicable.