An effective screening design for sensitivity analysis of large models

Campolongo, Francesca; Cariboni, Jessica; Saltelli, Andrea

doi:10.1016/j.envsoft.2006.10.004

Cited by 1,461 publications

(1,161 citation statements)

References 21 publications

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“…Based on this classification it can be concluded that the Elementary Effect method, which was proposed by Morris in 1991 [4], is a simple but effective screening method [5]. The studies in [6,7] also support this statement. In the category of the quantitative analysis methods, the Monte Carlo method for variance-based sensitivity analysis is more feasible than other methods, e.g., the Fourier Amplitude Sensitivity Test (FAST), as it requires no assumption and considers not only the linear relation between the input parameters and output variable but also the nonlinear relation [8].…”

Section: Introductionmentioning

confidence: 69%

“…The mean b µ k of the elementary effect for the k-th input parameter is a sensitivity measure proposed to assess the overall influence of the k-th input parameter on the output variable [6]. A relatively high value of b µ k indicates that the k-th input parameter has a significant influence on the result of the calculated output variable.…”

Section: Screening Methods: the Elementary Effect Methodsmentioning

confidence: 99%

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Approach to Prove the Efficiency of the Monte Carlo Method Combined With the Elementary Effect Method to Quantify Uncertainty of a Beam Structure With Piezo-Elastic Supports

Li¹,

Goetz²,

Schaeffner³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. In this paper, a new approach is presented to prove the efficiency of the direct Monte Carlo method combined with the Elementary Effect method to quantify structural data uncertainty under uncertain input parameters of a beam structure. Normally, the application of the direct Monte Carlo method requires high computational cost when all input parameters are taken into account. It is proposed to use a combination of the direct Monte Carlo method and the Elementary Effect method for the variance-based sensitivity analysis, named the combined Monte Carlo method. By the application of the Elementary Effect method as a screening method, the truely influential input parameters are identified. Then, the parametric uncertainty is analyzed only under these influential input parameters' uncertainty by the use of the Monte Carlo method. Through a combination of these two methods, the number of simulations can be significantly reduced due to the reduction of the number of analyzed input parameters.The novelty of this paper is to investigate the accuracy and the efficiency of this combined approach by the use of a beam structure with piezo-elastic supports for buckling and vibration control as a reference structure. The uncertain structural input parameters are the geometric, material, and stiffness parameters of the piezo-elastic supports. The output variable is the first lateral resonance frequency of the beam structure. Its uncertainty will be analyzed by the application of the combined Monte Carlo method applied for only a few but influential input parameters and will also be analyzed by the application of the direct Monte Carlo method for all input parameters. The results by the two methods will be compared based on the analysis accuracy to estimate the sensitivity of the input parameters on the first lateral resonance frequency and the minimal required number of the simulations.

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Section: Introductionmentioning

confidence: 69%

Section: Screening Methods: the Elementary Effect Methodsmentioning

confidence: 99%

Approach to Prove the Efficiency of the Monte Carlo Method Combined With the Elementary Effect Method to Quantify Uncertainty of a Beam Structure With Piezo-Elastic Supports

Li¹,

Goetz²,

Schaeffner³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

View full text Add to dashboard Cite

show abstract

“…In fact, it can be considered as an OAT method that is randomly applied in a defined number of R regions of the parameter space Ω which was defined by the intervals listed in Table 4. Campolongo et al (2007) also showed that the modified Morris method approximates the results provided by variance-based methods with significantly lower computational requirements. The elementary effects are estimated by comparing the variation of the model's output with the variation of a given parameter.…”

Section: Identification Of Sensitive Factors In the Dynamic Modelmentioning

confidence: 80%

“…Furthermore, the limited data availability discouraged a detailed identifiability analysis. The elementary effect method proposed by Morris (1991) and further improved by Campolongo et al (2007) is a compromise between a simple OAT approach and the more complex GSA methods. In fact, it can be considered as an OAT method that is randomly applied in a defined number of R regions of the parameter space Ω which was defined by the intervals listed in Table 4.…”

Section: Identification Of Sensitive Factors In the Dynamic Modelmentioning

confidence: 99%

“…a single value found for photodegradation rate for benzene) in literature, a ±100% "safety factor" was used to define the sampling interval, with an approach similar to that applied in chemical risk assessment (European Commission, 2003). The screening of the model factors was performed by generating 20 Morris trajectories (Campolongo et al, 2007) through applying the simplex-based method described in Pujol (2009 …”

Section: Identification Of Sensitive Factors In the Dynamic Modelmentioning

confidence: 99%

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Modelling the fate of organic micropollutants in stormwater ponds

Vezzaro

Eriksson

Ledin

et al. 2011

Science of The Total Environment

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Urban water managers need to estimate the potential removal of organic micropollutants (MP) in stormwater treatment systems to support MP pollution control strategies. This study documents how the potential removal of organic MP in stormwater treatment systems can be quantified by using multimedia models. The fate of four different MP in a stormwater retention pond was simulated by applying two steady-state multimedia fate models (EPI Suite and SimpleBox) commonly applied in chemical risk assessment and a dynamic multimedia fate model (Stormwater Treatment Unit Model for Micro Pollutants -STUMP). The four simulated organic stormwater MP (iodopropynyl butylcarbamate -IPBC, benzene, glyphosate and pyrene) were selected according to their different urban sources and environmental fate. This ensures that the results can be extended to other relevant stormwater pollutants. All three models use substance inherent properties to calculate MP fate but differ in their ability to represent the small physical scale and high temporal variability of stormwater treatment systems. Therefore the three models generate different results. A Global Sensitivity Analysis (GSA) highlighted that settling/resuspension of particulate matter was the most sensitive process for the dynamic model. The uncertainty of the estimated MP fluxes can be reduced by calibrating the dynamic model against total suspended solids data. This reduction in uncertainty was more significant for the substances with strong tendency to sorb, i.e. glyphosate and pyrene and less significant for substances with a smaller tendency to sorb, i.e. IPBC and benzene. The results provide support to the elaboration of MP pollution control strategies by limiting the need for extensive and complex monitoring campaigns targeting the wide range of specific organic MP found in stormwater runoff.Please cite the published version of this article as: . Modelling the fate of organic micropollutants in stormwater ponds.

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Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

Duan

Gong

et al. 2015

Geophysical Research Letters

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A global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting. Twenty-three adjustable parameters were selected from seven physical components of the WRF model. The sensitivity was evaluated based on skill scores calculated over nine 5 day precipitation forecasts during the summer seasons from 2008 to 2010 in the Greater Beijing Area in China. We found that eight parameters are more sensitive than others. Storm type seems to have no impact on the list of sensitive parameters but does influence the degree of sensitivity. We also examined the physical interpretation of parameter sensitivity. This analysis is useful for further optimization of the WRF model parameters to improve precipitation forecasting.

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An effective screening design for sensitivity analysis of large models

Cited by 1,461 publications

References 21 publications

Approach to Prove the Efficiency of the Monte Carlo Method Combined With the Elementary Effect Method to Quantify Uncertainty of a Beam Structure With Piezo-Elastic Supports

Approach to Prove the Efficiency of the Monte Carlo Method Combined With the Elementary Effect Method to Quantify Uncertainty of a Beam Structure With Piezo-Elastic Supports

Modelling the fate of organic micropollutants in stormwater ponds

Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

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