Explainability of Machine Learning Models with XGBoost and SHAP Values in the Context of Coping with Disasters

• Lucas Teixeira UFSCar
• Augusto Matos UFSCar
• Gabriel Carvalho UFSCar
• Norma Valencio UFSCar
• Heloisa Camargo UFSCar

Resumo

A considerable part of Brazilian municipalities experiences the recurrence of emergencies, related to different types of disasters, which weakens them – both the public administration and the citizens – while they are equally oriented to operate transitions to achieve the 2030 Sustainable Development Goals (SDG) Agenda. To mitigate the crises addressed by the 2030 Agenda, intersectoral consultation guided by an integrated approach to social justice, accountability and sustainability is required to renew social dynamics and corresponding local public policies. The objective of this paper is to carry out an integrated analysis of such dynamics in their main social and economic components, modeling the indicators of some specific SGD and emergence decrees data as input variables to a classification problem and using explainable machine learning, namely SHAP values technique, to assess their intertwining and/or synergies. Three classification models were tested on data from 2003 to 2020 and the model which presented the best accuracy was analyzed with SHAP values, revealing that different variables are decisive for each phenomenon associated with the disaster and their understanding allows elucidating critical points to be addressed by managers.