Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory

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Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory

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dc.contributor.author Fu, Guangtao en_US
dc.contributor.author Butler, David en_US
dc.contributor.author Khu, Soon-Thiam en_US
dc.contributor.author Sun, Si'ao en_US
dc.date.accessioned 2012-07-10T14:48:23Z en_US
dc.date.accessioned 2012-09-28T18:29:12Z en_US
dc.date.accessioned 2013-03-20T12:21:43Z
dc.date.issued 2011 en_US
dc.description.abstract Uncertainty analysis is widely applied in water system modeling to quantify prediction uncertainty from models and data. Conventional methods typically handle various kinds of uncertainty using a single characterizing approach, be it probability theory or fuzzy set theory. However, using a single approach may not be appropriate, particularly when uncertainties are of different types. For example, in sewer flood estimation problems, random rainfall variables are used as model inputs and imprecise or subjective information is used to define model parameters. This paper presents a general framework for sewer flood estimation that enables simultaneous consideration of two types of uncertainty: randomness from rainfall data represented using imprecise probabilities and imprecision from model parameters represented by fuzzy numbers. These two types of uncertainties are combined using random set theory and then propagated through a hydrodynamic urban drainage model. Two propagation methods, i.e., discretization and Monte Carlo based methods, are presented and compared, with the latter shown to be much more computationally efficient and hence recommended for high-dimensional problems. The model output (flood depth) is generated in the form of lower and upper cumulative probabilities, which are best estimates given the various stochastic and epistemic uncertainties considered and which embrace the unknown true cumulative probability. The distance between the cumulative probabilities represents the extent of imprecise, incomplete, or conflicting information and can be reduced only when more knowledge is available. The proposed methodology has a more complete and thus more accurate representation of uncertainty in data and models and can effectively handle different uncertainty characterizations in a single, integrated framework for sewer flood estimation. en_US
dc.identifier.citation Vol. 47, article W02534 en_US
dc.identifier.doi 10.1029/2009WR008944 en_US
dc.identifier.uri http://hdl.handle.net/10036/3835 en_US
dc.publisher American Geophysical Union en_US
dc.relation.url http://dx.doi.org/10.1029/2009WR008944 en_US
dc.subject flood analysis en_US
dc.subject fuzzy sets en_US
dc.subject imprecise probability en_US
dc.subject random sets en_US
dc.subject uncertainty analysis en_US
dc.subject urban drainage en_US
dc.title Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory en_US
dc.date.available 2012-07-10T14:48:23Z en_US
dc.date.available 2012-09-28T18:29:12Z en_US
dc.date.available 2013-03-20T12:21:43Z
exeter.article-number W02534 en_US
exeter.contacts.depositing-owner-email Butler, David <D.Butler@exeter.ac.uk> en_US
exeter.contacts.owner-email Fu, Guangtao <G.Fu@exeter.ac.uk> en_US
exeter.contacts.owner-email Butler, David <D.Butler@exeter.ac.uk> en_US
dc.description publication-status: Published en_US
dc.description types: Article en_US
dc.description Copyright © 2011 American Geophysical Union en_US
dc.identifier.journal Water Resources Research en_GB


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