Uporedite metode

	Average Annual Loss Estimation	HAZUS Loss Estimation
Oblast	Disaster Studies	Disaster Studies
Porodica	Process / pipeline	Process / pipeline
Godina nastanka	2005	2006
Tvorac	Patricia Grossi & Howard Kunreuther; Vitor Silva et al. (GEM)	Federal Emergency Management Agency; Charles Kircher, Robert Whitman & William Holmes
Tip	Expected-value risk metric computed from a loss exceedance distribution	Standardized GIS-based multi-hazard loss-estimation pipeline
Temeljni izvor	Grossi, P., & Kunreuther, H. (Eds.) (2005). Catastrophe Modeling: A New Approach to Managing Risk. Springer. ISBN: 9780387241050	Kircher, C. A., Whitman, R. V., & Holmes, W. T. (2006). HAZUS Earthquake Loss Estimation Methods. Natural Hazards Review, 7(2), 45-59.
Drugi nazivi	Annual Average Loss (AAL), Annualized Expected Loss, Pure Premium Estimation, Expected Annual Damage	Hazus-MH Loss Estimation, FEMA Hazus Methodology, Standardized Regional Loss Estimation, Hazus Earthquake Model
Srodne	4	4
Sažetak	Average annual loss (AAL) estimation computes the expected loss per year from a hazard, the long-run mean of annual losses obtained by weighting every possible event's loss by its annual frequency. It is the single most important summary statistic produced by probabilistic risk and catastrophe models, equal both to the frequency-weighted sum of event losses and to the area under the loss exceedance curve. Patricia Grossi and Howard Kunreuther's 2005 volume sets out how AAL and the exceedance curve are derived and used in risk management, and Vitor Silva and colleagues' 2020 global seismic risk model reports AAL (and AAL ratios) as its headline risk metric across the world. Because it is an expected value, AAL is additive across assets, perils, and regions, which makes it ideal for ranking risk, setting the technical (pure) insurance premium, and screening mitigation. Unlike return-period losses it says nothing about the tail, so it is the complement to probable maximum loss rather than a substitute. Estimating it correctly means handling both frequencies and the full range of event losses, including rare severe ones.	HAZUS loss estimation is FEMA's standardized, GIS-based methodology for estimating the physical, social, and economic consequences of earthquakes, floods, hurricanes, and tsunamis across a region. It chains together four conceptual modules, potential hazard, inventory of the built environment, direct physical damage, and induced and economic losses, so that a consistent national framework can produce comparable loss estimates anywhere in the United States. Charles Kircher, Robert Whitman, and William Holmes's 2006 paper documents the earthquake methodology, including its use of capacity-spectrum demand estimation and lognormal fragility curves, and FEMA's technical manuals specify every default inventory, fragility, and loss parameter. The system is distinguished less by methodological novelty than by standardization: it packages decades of earthquake and flood loss science into reproducible software with vetted defaults. Planners, emergency managers, and policymakers use it for scenario planning, mitigation prioritization, and disaster response. Because its defaults are transparent and documented, HAZUS is both a working tool and a reference implementation of regional loss estimation.