Fire Emergency Response Resilience in Interdependent Road–Water–Command Systems: A Bayesian Network Analysis

Abstract

Urban fire emergency response depends on the coordinated functioning of road access, firefighting water supply, and command systems. This study develops an 18-node Bayesian network to assess Response Resilience in interdependent road–water–command systems using 200 coded urban fire emergency cases. The model links 12 observable conditions to Road Capacity, Water Capacity, and Command Capacity, which jointly influence Response Effectiveness and Response Disruption Severity; these outcomes subsequently determine Response Resilience. Baseline inference indicates a 34% probability of effective response, a 42% probability of high Disruption Severity, and a 17% probability of high resilience. Scenario analysis shows that weak Command Capacity produces the most adverse single-subsystem outcome, while strong Command Capacity partially offsets simultaneous road and water constraints. Sensitivity analysis identifies Fire Lane Availability, Water Pressure, Water Coordination, Unified Command, Resource Mobilization, and Alarm Dispatch as influential nodes. The integrated intervention increases the effective response rate to 50%, reduces high Disruption Severity to 32%, and raises high resilience to 25%. A 1000-replication bootstrap supports the sampling stability of the principal estimates and intervention effects. The findings provide probabilistic decision support for coordinated urban fire–Response Resilience improvement.