Humanitarian crises such as natural disasters and armed conflicts are increasing in frequency and intensity, posing major challenges to the sustainable protection of vulnerable populations. Rapid and equitable identification of individuals at highest risk is essential for efficient allocation of limited emergency resources and for strengthening community resilience. This study proposes an intelligent, privacy-aware decision-support platform for citizen-level vulnerability assessment that supports social sustainability and resilient crisis management. The platform integrates heterogeneous data from healthcare institutions, municipal civil records, and emergency rescue services to construct multidimensional vulnerability profiles based on social conditions, medical status, and geographical accessibility. The dataset was collected in Algeria in collaboration with the Algerian Civil Protection and consolidated into a fully anonymised dataset of approximately 5000 individual records reflecting realistic crisis scenarios. Five supervised machine learning models (Decision Tree, Random Forest, Support Vector Machine (RBF), XGBoost, and Logistic Regression) were evaluated under class-imbalance conditions using SMOTE and class weighting. The Random Forest model achieved the best performance, with an F1-Macro score of 0.710 and a recall of 0.569 for the high-risk class (95% confidence interval: 0.431, 0.706). These results demonstrate that the proposed platform enables transparent, data-driven prioritisation of emergency interventions, contributing to sustainable humanitarian response, improved public resource allocation, and enhanced resilience of vulnerable communities.
Siari et al. (Thu,) studied this question.
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