Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Abstract

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.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

“…About 96% of the studies used supervised methods, two employed unsupervised methods ( Abarca-Alvarez, Reinoso-Bellido, & Campos-Sánchez, 2019 ; Crossley et al, 2020 ), and one study used a semi-supervised method ( Nguyen et al, 2017a ). Among supervised methods, classification and regression trees (CART) were the most common (n = 20), followed by random forests (n = 17).…”

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

“…Within risk, hazard, and disaster scholarship, vulnerability science has long encompassed three different but intersecting domains: physical/natural systems (e.g., exposure, intensity, frequency of occurrence), human systems including social systems and built environment (e.g., socio-demographic characteristics of at-risk populations, the degree of urbanization), and local spatial characteristics of places (e.g., location-specific conditions such as proximity to hazardous areas) [31,32] With the abundance and increasing accessibility of georeferenced big data, vulnerability and environmental sciences are evolving to incorporate new methodologies to handle increasingly complex datasets that describe the complexity of human-environment interactions and the dynamic characteristics of natural hazards [33]. This era of big data has led to advances in vulnerability research in estimating, predicting, and visualizing potential risk or vulnerability to natural hazards using large volumes of data and a variety of data-driven computing approaches [34][35][36][37]. Big data can be defined in a variety of ways depending on the disciplines and subjects being studied.…”

Assessment of the adequacy of mobile applications for disaster reduction

“…From this point of view, the use of a single application would optimise and accelerate response management (Park, 2017 ; Verrucci et al, 2016 ) and could strengthen local capacities among regions for disaster preparedness. Moreover, using artificial neural networks (Pashazadeh & Javan, 2020 ) and artificial intelligence (Abarca-Alvarez et al, 2019 ) for building forecasting models (Ogania et al, 2019 ; Williams & Lück-Vogel, 2020 ) could have a beneficial impact, and it could be adequately complemented by spatial–temporal detection systems (Yu et al, 2020 ) and the Internet of Things (Arshad et al, 2019 ; Xu et al, 2018 ).…”