Decision Model for Predicting Social Vulnerability Using Artificial Intelligence Abarca Álvarez, Francisco Javier Reinoso Bellido, Rafael Campos Sánchez, Francisco Sergio Social Vulnerability Predictive models Urban model Dwellings Decision model Artificial neural network Self-Organizing Maps Decision trees The APC was funded by their authors. Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information. 2020-04-03T08:37:00Z 2020-04-03T08:37:00Z 2019-12-19 info:eu-repo/semantics/article Abarca-Alvarez, F.J.; Reinoso-Bellido, R.; Campos-Sánchez, F.S. Decision Model for Predicting Social Vulnerability Using Artificial Intelligence. ISPRS Int. J. Geo-Inf. 2019, 8, 575. [doi:10.3390/ijgi8120575] http://hdl.handle.net/10481/61026 10.3390/ijgi8120575 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI