Сравнение методов

	Lifeline Interdependency Analysis	Disaster Recovery Curve Analysis
Область	Disaster Studies	Disaster Studies
Семейство	Process / pipeline	Process / pipeline
Год появления	2001	2006
Автор метода	Steven M. Rinaldi, James P. Peerenboom & Terrence K. Kelly	Scott B. Miles & Stephanie E. Chang
Тип	Network model of coupled critical-infrastructure systems and cascading failure	Time-trajectory model of functional recovery and resilience
Основополагающий источник	Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11-25.	Miles, S. B., & Chang, S. E. (2006). Modeling Community Recovery from Earthquakes. Earthquake Spectra, 22(2), 439-458.
Другие названия	Critical Infrastructure Interdependency Analysis, Infrastructure Interdependency Modeling, Lifeline Network Analysis	Recovery Trajectory Analysis, Resilience Curve Analysis, Community Recovery Modeling
Связанные	3	3
Сводка	Lifeline interdependency analysis studies how critical infrastructure systems — power, water, natural gas, telecommunications, and transportation — depend on one another, so that a disaster striking one can cascade into others. The foundational framework, set out by Steven Rinaldi, James Peerenboom, and Terrence Kelly in 2001, classifies the couplings among infrastructures into physical, cyber, geographic, and logical interdependencies and characterizes how disruptions propagate across them. Modeled as coupled networks, the lifelines and their dependency links allow analysts to simulate cascading failure: a power outage stops water pumping and telecommunications, transport disruption delays restoration, and feedbacks amplify the impact. The analysis estimates the system-wide consequences of disruptions and informs restoration sequencing, making it central to understanding why disasters disable far more than the directly damaged components and to planning resilient, recoverable infrastructure.	Disaster recovery curve analysis represents the recovery of a community or system after a disaster as a trajectory of functionality over time and uses that trajectory to quantify resilience. Building on the resilience-triangle concept and formalized for community recovery by Scott Miles and Stephanie Chang in 2006, the approach tracks a performance indicator — population, employment, housing occupancy, service capacity, or composite functionality — from its pre-event baseline, through the abrupt drop caused by the disaster, along the path back toward (or beyond) the baseline. From the curve, analysts read the magnitude of the initial loss, the speed and shape of recovery, the time to return, and the cumulative resilience loss represented by the area between the baseline and the recovery path. Comparing curves across communities or scenarios reveals what drives faster, fuller recovery and complements loss-estimation models that stop at the moment of impact.