Explainable machine learning for anticipating county-level disaster recovery burden in the built environment

Purpose This study aims to develop and evaluate an explainable machine-learning framework to anticipate county-level high recovery burden from natural disasters one year ahead using only public administrative and social-vulnerability data. Its substantive aim is to help built-environment decision makers shift from reactive post-event allocation to earlier, place-based prioritization of recovery and mitigation effort. Design/methodology/approach A county-year panel was assembled for 3,042 US counties over 2014–2022 by combining FEMA Disaster Declarations Summaries with county-level CDC/ATSDR social vulnerability index data. The outcome was a high-burden county-year, defined as repeated county-level declarations involving public assistance and/or hazard mitigation programme activity. Class-weighted logistic regression, random forest and XGBoost were benchmarked using a temporally ordered train-validation-test split. SHAP values and model coefficients were used to interpret predictive drivers. Findings The benchmark produced a moderate but decision-relevant predictive signal. Class-weighted logistic regression achieved the strongest out-of-sample performance on the hold-out period (area under the receiver operating characteristic curve 0.626; area under the precision–recall curve 0.140; F1-score 0.262) and outperformed the more flexible tree-based models. Across models, stronger prior programme burden, recent major declarations, population size, mobile-home prevalence and social vulnerability were associated with higher next-year burden. Research limitations/implications The dependent variable is an administrative proxy for recovery burden rather than a direct measure of physical loss, repair cost or household recovery outcomes. The empirical setting is the USA; transfer to other countries should therefore be treated as conceptual and methodological rather than statistical. Practical implications The framework offers a transparent annual screening tool for local governments, regional planning bodies and emergency-management agencies seeking to identify counties likely to face elevated recovery pressure. Social implications Because it relies on public tabular data rather than expensive proprietary inputs, it is potentially transferable to resource-constrained settings that need interpretable anticipatory analytics. Originality/value The paper contributes a reproducible public-data pipeline that links explainable artificial intelligence to disaster-resilience planning in the built environment. It also responds to the journal’s growing interest in technology-enabled resilience by showing that interpretable models, rather than only complex algorithms, can support forward-looking preparedness and recovery prioritization.