Local explainability of fuzzy and classic models focusing on disaster management in Brazilian municipalities - Ribeiro, Renata; Valencio, Norma; Camargo, Heloisa (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.14320, 2025)
Disasters are socio-environmental processes of losses and damages related to severe or extreme events. The lower the socio-spatial self-protection to deal with such events, the greater the chances of a disaster occurring. In Brazil, when the local damages and losses incurred exceed the own resources from the municipal administration to assist the affected people, the triggering of declaration of emergency issued by Brazilian municipalities is required as a legal instrument to obtain appropriate external support. In this article, we develop a study that applies machine learning and explainable AI techniques to generate fuzzy and classic classification models and interpret predictions with a data set that relates the declaration of emergencies and indicators corresponding to Sustainable Development Goals 1, 3, 6 and 10, from 2016 to 2022. A qualitative analysis was performed on the results provided by the explainable AI techniques that led to the identification of indicators that have the greatest influence on predictions and provided additional support to field researchers and decision makers in the context of disaster response.