Subsurface Characterization and Monitoring Techniques: A by J. Russell Boulding PDF

By J. Russell Boulding

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Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and their Application, Cambridge: Cambridge University Press. DeGroot, M. H. and Schervish, M. J. (2002) Probability and Statistics, 3rd edn, Reading, MA: Addison-Wesley. de Haan, L. and Ferreira, A. (2006) Extreme Value Theory: An Introduction, New York, NY: Springer. Evans, M. and Swartz, T. (2000) Approximating Integrals via Monte Carlo and Deterministic Methods, Oxford: Oxford University Press. Gigerenzer, G. (2003) Reckoning with Risk: Learning to Live with Uncertainty, London: Penguin Books Ltd.

The natural hazard risk manager has to operate on time intervals of 30 years or more, over which many aspects of the hazard loss can be expected to change substantially; this is the problem of non-stationarity. For example, increasing populations result in increasing city size, increasing population density in cities, changes in the quality of buildings and infrastructure, changes in land-surface characteristics in catchments. In this situation historical losses are not a reliable guide to future losses over the whole of the time interval, and additional judgements are needed to perform the extrapolation.

Likewise with a hurricane, although in this case each component of the time-series might be a spatial map of wind velocities. The set of all possible values of ω is denoted as Ω. In Step 3 it is important to distinguish between a hazard event and a hazard outcome. The hazard outcome is the collection of hazard events that occur in the hazard domain. Therefore {(t, ω), (t′, ω′)} would be a hazard outcome comprising two events: at time t, ω happened, and at time t′, ω′ happened. The collection of all possible hazard outcomes is very large, and, from the risk manager’s point of view, each outcome must be assigned a probability.

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Subsurface Characterization and Monitoring Techniques: A Desk Reference Guide by J. Russell Boulding

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