Sensitivity analysis of microbial growth parameter distributions with respect to data quality and quantity by using Monte Carlo analysis

2004 ◽  
Vol 65 (3) ◽  
pp. 231-243 ◽  
Author(s):  
F. Poschet ◽  
K. Bernaerts ◽  
A.H. Geeraerd ◽  
N. Scheerlinck ◽  
B.M. Nicolaı̈ ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Data Quality ◽  
Microbial Growth ◽  
Growth Parameter ◽  
Monte Carlo Analysis ◽  
Parameter Distributions

scholarly journals Applying Monte Carlo and classification tree sensitivity analysis to the Zayandehrood River

2011 ◽  
Vol 14 (1) ◽  
pp. 236-250 ◽  
Author(s):  
Nader Nakhaei ◽  
Amir Etemad-Shahidi
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Decay Rate ◽  
Classification Tree ◽  
Monte Carlo Analysis ◽  
Water Quality Modeling ◽  
Classification Tree Analysis ◽  
Tree Analysis ◽  
Quality Modeling

Water quality modeling is an important issue for both engineers and scientists. The QUAL2K model is a simulation tool that has been used widely for this purpose. Uncertainty and sensitivity analysis is a major step in water quality modeling. This article reports application of Monte Carlo analysis and classification tree sensitivity analysis in the modeling of the Zayandehrood River. First the model was calibrated and validated using two sets of data. Then, three input values (stream flow, roughness and decay rate) were considered for both analyses. The Monte Carlo analysis was conducted using triangular distribution of probability of occurrence for the input parameters. The classification tree analysis classifies outcome values into non-numeric categories. Considering the relationships between the input parameters in the classification tree analysis is the most important advantage of it. The analyses demonstrated the key input variables for three points of the river. The dissolved oxygen levels were mainly sensitive to the decay rate coefficient along the river.

Sensitivity analysis of an urban stormwater microorganism model

2010 ◽  
Vol 62 (6) ◽  
pp. 1393-1400 ◽  
Author(s):  
D. T. McCarthy ◽  
A. Deletic ◽  
V. G. Mitchell ◽  
C. Diaper
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Analysis ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Urban Stormwater ◽  
Urban Catchments ◽  
Two Parameters

This paper presents the sensitivity analysis of a newly developed model which predicts microorganism concentrations in urban stormwater (MOPUS—MicroOrganism Prediction in Urban Stormwater). The analysis used Escherichia coli data collected from four urban catchments in Melbourne, Australia. The MICA program (Model Independent Markov Chain Monte Carlo Analysis), used to conduct this analysis, applies a carefully constructed Markov Chain Monte Carlo procedure, based on the Metropolis-Hastings algorithm, to explore the model's posterior parameter distribution. It was determined that the majority of parameters in the MOPUS model were well defined, with the data from the MCMC procedure indicating that the parameters were largely independent. However, a sporadic correlation found between two parameters indicates that some improvements may be possible in the MOPUS model. This paper identifies the parameters which are the most important during model calibration; it was shown, for example, that parameters associated with the deposition of microorganisms in the catchment were more influential than those related to microorganism survival processes. These findings will help users calibrate the MOPUS model, and will help the model developer to improve the model, with efforts currently being made to reduce the number of model parameters, whilst also reducing the slight interaction identified.

Sensitivity Analysis of a Generic Wake-Body Model for the Vortex-Induced Vibration of a Rigid Circular Cylinder Using Monte Carlo Simulations

2011 ◽  
Author(s):  
Rene D. Gabbai ◽  
Jonathan Hiebert
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Circular Cylinder ◽  
Uniform Distribution ◽  
Monte Carlo Analysis ◽  
Point Of View ◽  
Vortex Induced Vibration ◽  
Body Model ◽  
Marginal Probability Distribution ◽  
Steady State Amplitude

A Monte Carlo analysis using pseudo-random sampling is carried out with the objective of determining the relative importance of each of k = 5 independent input parameters appearing in a typical wake-body model for the vortex-induced vibration of an elastically-mounted rigid circular cylinder in uniform flow. For simplicity, the marginal probability distribution of each parameter is assumed to be a uniform distribution. Furthermore, the standard deviation of each distribution is assumed to be the same. The choice of the uniform distribution is also a reflection of the fact that exact forms for the distributions are not known. The sensitivity analysis indicates that the most important factor from the point of view of the predicted steady-state amplitude of oscillation of the cylinder yo is parameter M, which represents the scaling of the effect of the wake on the structure.

scholarly journals Investment Project of Sekala: Should Sekala Open Up a New Outlet?

2021 ◽  
Vol 1 (2) ◽  
pp. 01-09
Author(s):  
Fario Pranadi Prakoso ◽  
Sylviana Maya Damayanti
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Coffee Consumption ◽  
Investment Project ◽  
Capital Budgeting ◽  
Monte Carlo Analysis ◽  
Feasibility Analysis ◽  
Cost Ratio ◽  
The World

Coffee is one of the most consumed beverages in the world, coffee consumption in the world and Indonesia is growing rapidly year to year. Even in the pandemic, the demand for coffee is still high. This was the reason that there are so many new coffee shops, but some of them did not last long, the main reason was they are not preparing the calculation precisely. Sekala was planning to open up a new outlet in Bandung, with the measurement projection using capital budgeting from the year 2022-2026. This was required to measure whether the project is feasible or not. The project will be considered feasible if it meets certain analysis that was provided by feasibility analysis, sensitivity analysis, and Monte Carlo simulation. Feasibility analysis is required to show whether the projection is feasible or not. Sensitivity analysis is used to measure some criteria that affect the most Sekala in terms of the financial side, later on, it will be used on Monte Carlo analysis to see the project feasibility based on several simulations. The initial investment for this project is IDR 242,177,275.60, and the result of this projection is, payback period of 1.22 years, an estimated NPV of IDR 513,913,208.73, benefit/cost ratio of 3.12, and an internal rate of return of 52.04%. The sensitivity analysis result shows that the 4 most influential criteria are affecting the business. Monte Carlo simulation results show that this project has a high chance probability of giving NPV<0, for 40-55%.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

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