Modeling for Seismic Vulnerability Prediction Based on Physical-Spatial Indicators of Cities.( Case Study: Tabriz Region 8)

Journal of Radar and Optical Remote Sensing and GIS

مقاله 5، دوره 3، شماره 1، خرداد 2020، صفحه 55-75   

نوع مقاله: مقاله پژوهشی

نویسندگان

belal mohammadian^{* 1}؛ hasan ahar²؛ Ali Panahi³

¹Ms in GIS, Remote Sensing,Yazd Branch, Islamic Azad University, Yazd, Iran.

²PhD student in Geography and Urban Planning, Kharazmi University, Tehran, Iran

³PhD in Geography and Urban Planning, Azad Tabriz University, Tabriz, Iran

چکیده

Nowadays, given the growing trend of population and population density in urban areas especially in densely populated cities and in terms of seismicity, the need for a comprehensive and comprehensive approach to natural and catastrophic events resulting from their occurrence has become more apparent. In this study, District 8 of Tabriz was selected as a case study. The research method is descriptive-analytical, The purpose of this study is to evaluate and model for predicting seismic vulnerability based on physical-spatial indices of Tabriz Considering the quality criteria and providing a suitable model and model for construction in Tabriz, For this purpose, out of 9 criteria, building density, population density, quality of buildings (accessibility), access to pedestrian networks, access to essentials, access to open and green spaces, land use, size of fragments, and distance. Dangerous centers have been used. Necessary statistical data is collected, then layers are prepared for seismic zoning and weighting to layers is done according to zoning standards, the final analysis was performed using the combination of AHP and Fuzzy TOPSIS methods in GIS (proposed method). Finally, the risk level of the study area was obtained from Tabriz city. The results show that the northern areas have the highest risk areas and the southern areas have the least rate, and finally it is concluded that the proposed model for risk zoning is applicable.

کلیدواژه‌ها

Zoning؛ Earthquake؛ Tabriz؛ Fuzzy Logic؛ Multi Criteria Decision Making Models