scholarly journals Sensitivity Analysis to Uncertain Parameters of Topmodel in Tropical Regions with Application to the Middle Magdalena Valley

2018 ◽  
Author(s):  
Maria Cristina Arenas Bautista ◽  
Duque-Gardeazabal Nicolas ◽  
Arboleda-Obando Pedro Felipe ◽  
Guadagnini Alberto ◽  
Riva Monica ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Uncertain Parameters ◽  
Tropical Regions

Global sensitivity analysis of statistical models by double randomization method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Dmitriy Kolyukhin
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Models ◽  
Global Sensitivity Analysis ◽  
Uncertain Parameters ◽  
Suggested Approach ◽  
Global Sensitivity ◽  
Double Randomization Method ◽  
Complex Models ◽  
Monte Carlo Estimation ◽  
Randomization Method

Abstract The paper addresses a global sensitivity analysis of complex models. The work presents a generalization of the hierarchical statistical models where uncertain parameters determine the distribution of statistical models. The double randomization method is applied to increase the efficiency of the Monte Carlo estimation of Sobol indices. Numerical computations are provided to study the accuracy and efficiency of the proposed technique. The issue of optimization of the suggested approach is considered.

MILP Sensitivity Analysis for Short-Term Scheduling of Batch Plants under Uncertainty

2011 ◽  
Vol 181-182 ◽  
pp. 577-582
Author(s):  
Jin Zhu ◽  
Xiu Mei Zhang ◽  
Wei Kang
Keyword(s):  
Sensitivity Analysis ◽  
Branch And Bound ◽  
Parametric Programming ◽  
Uncertain Parameters ◽  
Deterministic Case ◽  
Short Term ◽  
Batch Plants ◽  
Bound Solution ◽  
Solution Methodology

In this paper, An integrated framework is developed to handle uncertainty in short-term scheduling based on the idea of inference-based sensitivity analysis for MILP problems and the utilization of a branch and bound solution methodology. The proposed method leads to the determination of the importance of different parameters and the constraints on the objective function and the generation and evaluation of a set of alternative schedules given the variability of the uncertain parameters. The main advantage of the proposed method is that no substantial complexity is added compared with the solution of the deterministic case because the only additional required information is the dual information at the leaf nodes of the branch-and-bound tree. Two case studies are presented to highlight the information extracted by the proposed approach and the complexity involved compared with parametric programming studies.

The Fuzzy Reliability Analysis for the Super Wide Box Girder of New Jiujiang Changjiang River Bridge

2013 ◽  
Vol 680 ◽  
pp. 157-160
Author(s):  
Yao Rong ◽  
Hong Yuan Huang ◽  
Xiao Qin Ouyang
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Crack Width ◽  
Concrete Strength ◽  
Uncertain Parameters ◽  
Changjiang River ◽  
Fuzzy Reliability ◽  
Box Girder ◽  
Crack Control ◽  
Design Specifications

The crack control of the concrete super wide box girder for New Jiujiang Changjiang River Bridge is effected by various uncertain factors such as loads, concrete strength and the release of hydration heat etc. Because of the fuzziness for the crack control of the super wide box girder, it is difficult to accurately calculate the crack width to meet with the requirement of the limiting crack design for the structure. Based on the maximal permit crack formula issued in “Concrete Design Specifications”, the fuzziness analysis is made to the crack control reliability, the index of fuzzy reliable degrees of the girder maximal permit crack width is calculated by Monte Carlo method, and the sensitivity analysis to uncertain parameters is given to provide reference of the design for River-Crossing Bridge.

scholarly journals Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

2011 ◽  
Vol 11 (7) ◽  
pp. 20433-20485 ◽  
Author(s):  
L. A. Lee ◽  
K. S. Carslaw ◽  
K. Pringle ◽  
G. W. Mann ◽  
D. V. Spracklen
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Interaction Effects ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Model Output ◽  
Aerosol Model ◽  
Uncertainty Space ◽  
Gaussian Process Emulation ◽  
Gaussian Process Emulator

Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric model.

A Comparison of Probability Bounds Analysis and Sensitivity Analysis in Environmentally Benign Design and Manufacture

2006 ◽  
Author(s):  
Jason Matthew Aughenbaugh ◽  
Scott Duncan ◽  
Christiaan J. J. Paredis ◽  
Bert Bras
Keyword(s):  
Sensitivity Analysis ◽  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Environmentally Benign ◽  
Cumulative Probability ◽  
Uncertain Parameters ◽  
Probability Bounds ◽  
Irreducible Uncertainty ◽  
Design And Manufacture ◽  
Selection Of

There is growing acceptance in the design community that two types of uncertainty exist: inherent variability and uncertainty that results from a lack of knowledge, which variously is referred to as imprecision, incertitude, irreducible uncertainty, and epistemic uncertainty. There is much less agreement on the appropriate means for representing and computing with these types of uncertainty. Probability bounds analysis (PBA) is a method that represents uncertainty using upper and lower cumulative probability distributions. These structures, called probability boxes or just p-boxes, capture both variability and imprecision. PBA includes algorithms for efficiently computing with these structures under certain conditions. This paper explores the advantages and limitations of PBA in comparison to traditional decision analysis with sensitivity analysis in the context of environmentally benign design and manufacture. The example of the selection of an oil filter involves multiple objectives and multiple uncertain parameters. These parameters are known with varying levels of uncertainty, and different assumptions about the dependencies between variables are made. As such, the example problem provides a rich context for exploring the applicability of PBA and sensitivity analysis to making engineering decisions under uncertainty. The results reveal specific advantages and limitations of both methods. The appropriate choice of an analysis depends on the exact decision scenario.

scholarly journals Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

2011 ◽  
Vol 11 (23) ◽  
pp. 12253-12273 ◽  
Author(s):  
L. A. Lee ◽  
K. S. Carslaw ◽  
K. J. Pringle ◽  
G. W. Mann ◽  
D. V. Spracklen
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Interaction Effects ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Model Output ◽  
Atmospheric Models ◽  
Aerosol Model ◽  
Gaussian Process Emulation ◽  
Gaussian Process Emulator

Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.

The Fuzzy Reliability Analysis for the Thin-Walled High Pier of SanBei Bridge

2013 ◽  
Vol 838-841 ◽  
pp. 1092-1095
Author(s):  
Yun Lai Liu ◽  
Wen Bi ◽  
Yao Rong
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Crack Width ◽  
Concrete Strength ◽  
Uncertain Parameters ◽  
Fuzzy Reliability ◽  
Crack Control ◽  
Design Specifications ◽  
Thin Walled ◽  
High Pier

The crack control of the thin-walled high pier for SanBei Bridge is effected by various uncertain factors such as loads, concrete strength and the release of hydration heat etc. Because of the fuzziness for the crack control of thin-walled high pier, it is difficult to accurately calculate the crack width to meet with the requirement of the limiting crack design for the structure. Based on the maximal permit crack formula issued in Concrete Design Specifications, the fuzziness analysis is made to the crack control reliability, the index of fuzzy reliable degrees of the girder maximal permit crack width is calculated by Monte Carlo method, and the sensitivity analysis to uncertain parameters is given to provide reference of the design for SanBei Bridge.

scholarly journals Coarse woody debris decomposition assessment tool: Model development and sensitivity analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251893
Author(s):  
Zhaohua Dai ◽  
Carl C. Trettin ◽  
Andrew J. Burton ◽  
Martin F. Jurgensen ◽  
Deborah S. Page-Dumroese ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
North America ◽  
Coarse Woody Debris ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Woody Debris ◽  
Geographical Location ◽  
Ambient Conditions ◽  
Decomposition Rates ◽  
Tropical Regions

Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m-2), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia.

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