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First‐order reliability approach to stochastic analysis of subsurface flow and contaminant transport
Author(s) -
Sitar Nicholas,
Cawlfield Jeffrey D.,
Der Kiureghian Armen
Publication year - 1987
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr023i005p00794
Subject(s) - reliability (semiconductor) , monte carlo method , random variable , probabilistic logic , first order reliability method , flow (mathematics) , marginal distribution , computer science , joint probability distribution , mathematical optimization , stochastic process , probability distribution , statistics , mathematics , geometry , quantum mechanics , power (physics) , physics
The first‐order reliability method is an attractive approach to stochastic analysis of subsurface flow and contaminant transport. The method can be used with either analytical or numerical solutions, allowing a uniform but flexible approach to solving a variety of problems, and it can fully utilize any level of probabilistic information from the minimum knowledge of second moments to complete knowledge of the full joint distribution. Therefore the first‐order reliability method is particularly useful when statistical information is incomplete, as is common for problems in the subsurface environment. Additionally, correlation and nonnormal marginal distributions may be incorporated into the solution. Results from a first‐order reliability analysis include an estimate of the probability of exceeding a specified performance criteria and measures of sensitivity of the stochastic solution to changes in random variables and their statistical moments. Three subsurface flow and contaminant transport example problems are used to illustrate the capabilities of the method; results from these examples compare well with previously published Monte Carlo simulation results.