Abstract
Abstract. Despite years of research on natural hazards and efforts to reduce physical and psychological damage, earthquake as a natural disaster is catastrophic. Though, human is the main axis in dealing with crisis and vulnerability, and since the space of cities encompasses largest population spectrum, managing this space is considered as an essential issue. Accordingly, the vulnerability of the City of Sanandaj was defined by environmental, physical and social criteria. In this regard, with the aim of modeling, and assessing the risk and vulnerability, the MCDA-ANN hybrid model was introduced as a new method for teaching of learning models. In order to determine the final value of each of the criteria, AHP analysis was performed as one of the MCDA methods to solve complex and non-structural problems by creating a functional hierarchy, and after that, a training data base for learning ANN was created randomly based on the AHP classification map. Then, for modeling, the radial base functional neural network (RBFNN) was used as one of the techniques of artificial neural networks. After the modeling, 30% of the points were selected as validation data to determine the accuracy of the model. After the implementation of RBFNN model, the area of AUC curve resulted is 0.922, which indicates the high accuracy of the model in assessing the risk of an earthquake. The results show high vulnerability in urban areas1 and 2 and in downtown Sanandaj that in these zones the physical and social factors dramatically affect the vulnerability of these areas.
Highlights
One of the main causes of urban vulnerability to earthquakes is the lack of control of urban growth, the lack of proper crisis management, and the construction of homes in risky places (Hashemi M et al, 2017)
In order to combine various factors and provide classified maps, artificial neural network (ANN) is a useful technology for zoning and risk assessment based on the training database, which has been used in many studies (Bouktif S et al, 2018 & Alizadeh M et al, 2018 & Yingying Tian et al, 2018)
Using a map resulted from the AHP model along with five classes; a database of training site for learning the radial base functional neural network (RBFNN) model was prepared under supervision
Summary
One of the main causes of urban vulnerability to earthquakes is the lack of control of urban growth, the lack of proper crisis management, and the construction of homes in risky places (Hashemi M et al, 2017). In order to combine various factors and provide classified maps, artificial neural network (ANN) is a useful technology for zoning and risk assessment based on the training database, which has been used in many studies (Bouktif S et al, 2018 & Alizadeh M et al, 2018 & Yingying Tian et al, 2018). The basis for the risk assessment and modeling is the selection of effective layers For this reason, in this research, using the opinions of engineers, researchers, previous studies and questionnaires, 13 layers from natural, physical and social criteria for modeling earthquake hazard in Sanandaj were selected. The main objective of this study is the zoning and evaluation of physical and social vulnerability to earthquakes that using of AHP and RBFNN models, one hybrid model has been developed based on effective indicators. Using a map resulted from the AHP model along with five classes; a database of training site for learning the RBFNN model was prepared under supervision
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
