Real-Time Predictive Analytics for Enhanced Emergency Response Systems

Abstract

Emergency response leaders make high stakes decisions while information is incomplete, noisy, and constantly changing. Many current platforms emphasize reactive monitoring and after action reporting, which leaves limited room for forecasting and proactive staging. This paper presents a real time predictive analytics approach that turns streaming signals into short horizon forecasts, actionable risk scores, and transparent recommendations. The proposed method combines event time feature engineering, probabilistic prediction with uncertainty estimates, and adaptive decision support rules that remain accountable to human judgment. A multi scenario evaluation shows improvements in dispatch timeliness, resource utilization, and outcome stability, while maintaining interpretable outputs that responders can trust under stress