Abstract:
The objective was to apply artificial neural networks to classify municipalities (counties) in Rio Grande do Norte State, Brazil, according to their social vulnerability. This was an ecological study using 17 variables that reflected epidemiological, demographic, socioeconomic, and educational indicators for the year 2010. The sources were the Human Development Atlas for Brazil and the Brazilian Institute of Geography and Statistics. For classification of the municipalities, the study applied the artificial neural networks of the PNN and Multilayer feedforward types, resulting in a classification in five categories of vulnerability: very high, high, medium, low, and very low. The networks’ training phase used the minimum and maximum values, 25th and 75th percentiles, and medians for the 17 selected variables. The Multilayer feedforward network with six nodes showed the best results. The municipalities from the Metropolitan Area (Natal, Parnamirim) and the eastern and western Seridó micro-regions (Caicó, Currais Novos, São José do Seridó, Jardim do Seridó, Parelhas, Carnaúba dos Dantas) showed the lowest levels of vulnerability. The municipalities with high and very high vulnerability were located in the East of the state, in the micro-regions of the Northeast Coast (João Câmara, Touros, Caiçara do Rio dos Ventos) and Southern Coast (Nísia Floresta, São José do Mipibu, Arês, Canguaretama). The neural network classified the municipalities with high precision, distinguishing those with extreme vulnerability from those with better social indicators.
Keywords:
Computer Neural Networks; Social Vulnerability; Social Indicators
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