Developing a Data-Driven Framework for Integrating Community Risk Assessment, Early Warning Systems, Emergency Resource Allocation and Post-Disaster Recovery Planning

Abstract

This study develops and tests a modular analytical framework that links community hazard exposure, socioeconomic vulnerability, forecasting, early-warning access, emergency resource allocation and post-disaster recovery priorities. Rather than assuming operational effectiveness from model complexity, the study evaluates whether linked decisions improve needs coverage under a constrained resource budget. A completely synthetic, transparent experiment creates 720 community– scenario records representing 120 artificial communities and six hazard scenarios each. A held-out set of 30 communities is used to compare logistic regression, random forests and gradient boosting for incident classification. Four allocation rules are then evaluated using identical budgets, minimum local service floors, and needs caps. The integrated rule employs vulnerability-weighted expected need and delivery efficiency informed by early-warning reach and distance. Outcome measures are average precision, ROC-AUC, Brier score, weighted service coverage, unmet need and an explicitly constructed recovery-time proxy. Findings demonstrate model-dependent predictive tradeoffs and policy-dependent allocation outcomes, but do not constitute evidence from actual disasters. The results establish a reproducible computational test bed for connecting forecasting and operational planning, while demonstrating the importance of distributional fairness, sensitivity testing, uncertainty documentation and field calibration before deployment.