Latin hypercube sampling and the sensitivity analysis of a Monte Carlo epidemic model

1988 ◽  
Vol 23 (1-2) ◽  
pp. 97-112 ◽  
Author(s):  
Susan K. Seaholm ◽  
Eugene Ackerman ◽  
Shu-Chen Wu
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Epidemic Model ◽  
Latin Hypercube Sampling ◽  
Latin Hypercube

scholarly journals Groundwater pollution risk using a modified Latin hypercube sampling

2006 ◽  
Vol 8 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Husam Baalousha
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Pollution ◽  
Probabilistic Models ◽  
Latin Hypercube Sampling ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Deterministic Models ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

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