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Application of uncertainty analysis to groundwater pollution modeling

Bobba, A Gosh ; Singh, Vijay P and Bengtsson, Lars LU (1995) In Environmental Geology p.89-96
Abstract
Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival... (More)
Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival time. Examples are provided that demonstrate: (a) confidence limits around predicted values of concentration and arrival time can be obtained, (b) the selection of probability distributions for input parameters affects the output variables, and (c) the probability distribution of the output variables can be different from that of the input variables, even when all input parameters have the same probability distribution. (Less)
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author
; and
organization
alternative title
Tillämpning av osäkerhetsanalys vid grundvattenmodellering
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Geology
pages
8 pages
external identifiers
  • scopus:0029412268
DOI
10.1007/BF00768321
language
English
LU publication?
yes
id
df140ab7-5fad-4513-a2d2-e7c5895024f7
date added to LUP
2023-06-15 18:18:08
date last changed
2023-06-19 13:07:51
@article{df140ab7-5fad-4513-a2d2-e7c5895024f7,
  abstract     = {{Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival time. Examples are provided that demonstrate: (a) confidence limits around predicted values of concentration and arrival time can be obtained, (b) the selection of probability distributions for input parameters affects the output variables, and (c) the probability distribution of the output variables can be different from that of the input variables, even when all input parameters have the same probability distribution.}},
  author       = {{Bobba, A Gosh and Singh, Vijay P and Bengtsson, Lars}},
  language     = {{eng}},
  pages        = {{89--96}},
  series       = {{Environmental Geology}},
  title        = {{Application of uncertainty analysis to groundwater pollution modeling}},
  url          = {{http://dx.doi.org/10.1007/BF00768321}},
  doi          = {{10.1007/BF00768321}},
  year         = {{1995}},
}