Sensitivity analysis and uncertainty estimation for nanoscale MOSFET

2018 ◽  
Vol 31 (5) ◽  
pp. e2346
Author(s):  
Panpan Yu ◽  
Ling Sun ◽  
Jiali Cheng ◽  
Jianjun Gao
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Estimation

scholarly journals Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology

2007 ◽  
Vol 11 (4) ◽  
pp. 1249-1266 ◽  
Author(s):  
M. Ratto ◽  
P. C. Young ◽  
R. Romanowicz ◽  
F. Pappenberger ◽  
A. Saltelli ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Estimation ◽  
Mechanistic Modeling ◽  
Structure Identification ◽  
Catchment Management ◽  
Effective Rainfall ◽  
Combined Approach ◽  
Top Down ◽  
Bottom Up ◽  
Modelling Methodology

Abstract. In this paper, we discuss a joint approach to calibration and uncertainty estimation for hydrologic systems that combines a top-down, data-based mechanistic (DBM) modelling methodology; and a bottom-up, reductionist modelling methodology. The combined approach is applied to the modelling of the River Hodder catchment in North-West England. The top-down DBM model provides a well identified, statistically sound yet physically meaningful description of the rainfall-flow data, revealing important characteristics of the catchment-scale response, such as the nature of the effective rainfall nonlinearity and the partitioning of the effective rainfall into different flow pathways. These characteristics are defined inductively from the data without prior assumptions about the model structure, other than it is within the generic class of nonlinear differential-delay equations. The bottom-up modelling is developed using the TOPMODEL, whose structure is assumed a priori and is evaluated by global sensitivity analysis (GSA) in order to specify the most sensitive and important parameters. The subsequent exercises in calibration and validation, performed with Generalized Likelihood Uncertainty Estimation (GLUE), are carried out in the light of the GSA and DBM analyses. This allows for the pre-calibration of the the priors used for GLUE, in order to eliminate dynamical features of the TOPMODEL that have little effect on the model output and would be rejected at the structure identification phase of the DBM modelling analysis. In this way, the elements of meaningful subjectivity in the GLUE approach, which allow the modeler to interact in the modelling process by constraining the model to have a specific form prior to calibration, are combined with other more objective, data-based benchmarks for the final uncertainty estimation. GSA plays a major role in building a bridge between the hypothetico-deductive (bottom-up) and inductive (top-down) approaches and helps to improve the calibration of mechanistic hydrological models, making their properties more transparent. It also helps to highlight possible mis-specification problems, if these are identified. The results of the exercise show that the two modelling methodologies have good synergy; combining well to produce a complete joint modelling approach that has the kinds of checks-and-balances required in practical data-based modelling of rainfall-flow systems. Such a combined approach also produces models that are suitable for different kinds of application. As such, the DBM model considered in the paper is developed specifically as a vehicle for flow and flood forecasting (although the generality of DBM modelling means that a simulation version of the model could be developed if required); while TOPMODEL, suitably calibrated (and perhaps modified) in the light of the DBM and GSA results, immediately provides a simulation model with a variety of potential applications, in areas such as catchment management and planning.

scholarly journals Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer

2019 ◽  
Vol 23 (2) ◽  
pp. 1103-1112 ◽  
Author(s):  
Weifei Yang ◽  
Changlai Xiao ◽  
Xiujuan Liang
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Measurement Errors ◽  
Uncertainty Estimation ◽  
Separation Method ◽  
Analytical Sensitivity ◽  
Hydrograph Separation ◽  
Time Step ◽  
Sensitivity Indices ◽  
Two Component

Abstract. The two-component hydrograph separation method with conductivity as a tracer is favored by hydrologists owing to its low cost and easy application. This study analyzes the sensitivity of the baseflow index (BFI, long-term ratio of baseflow to streamflow) calculated using this method to errors or uncertainties in two parameters (BFC, the conductivity of baseflow, and ROC, the conductivity of surface runoff) and two variables (yk, streamflow, and SCk, specific conductance of streamflow, where k is the time step) and then estimates the uncertainty in BFI. The analysis shows that for time series longer than 365 days, random measurement errors in yk or SCk will cancel each other out, and their influence on BFI can be neglected. An uncertainty estimation method of BFI is derived on the basis of the sensitivity analysis. Representative sensitivity indices (the ratio of the relative error in BFI to that of BFC or ROC) and BFI′ uncertainties are determined by applying the resulting equations to 24 watersheds in the US. These dimensionless sensitivity indices can well express the propagation of errors or uncertainties in BFC or ROC into BFI. The results indicate that BFI is more sensitive to BFC, and the conductivity two-component hydrograph separation method may be more suitable for the long time series in a small watershed. When the mutual offset of the measurement errors in conductivity and streamflow is considered, the uncertainty in BFI is reduced by half.

Uncertainty estimation of mechanical testing properties using sensitivity analysis and stochastic modelling

Measurement ◽  
2015 ◽  
Vol 62 ◽  
pp. 149-154 ◽  
Author(s):  
Salah Bouhouche ◽  
Slimane Ziani ◽  
Zoheir Mentouri ◽  
Jurgen Bast
Keyword(s):  
Sensitivity Analysis ◽  
Mechanical Testing ◽  
Stochastic Modelling ◽  
Uncertainty Estimation

scholarly journals Sensitivity analysis and uncertainty estimation for tephra dispersal models

2008 ◽  
Vol 113 (B6) ◽  
Author(s):  
Simona Scollo ◽  
Stefano Tarantola ◽  
Costanza Bonadonna ◽  
Mauro Coltelli ◽  
Andrea Saltelli
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Estimation ◽  
Tephra Dispersal

scholarly journals Technical note: Analytical sensitivity analysis and uncertainty estimation of a two-component hydrograph separation method which uses conductivity as a tracer

2018 ◽  
Author(s):  
Weifei Yang ◽  
Changlai Xiao ◽  
Xiujuan Liang
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Relative Error ◽  
Measurement Errors ◽  
The United States ◽  
Uncertainty Estimation ◽  
Separation Method ◽  
Hydrograph Separation ◽  
Sensitivity Indices ◽  
Two Component

Abstract. The conductivity two-component hydrograph separation method is cheap and easy to operate and is favored by hydrologists. This paper analyzes the sensitivity of the baseflow index (BFI, the long-term ratio of baseflow to streamflow) calculated by this method to errors or uncertainties of the two parameters (BFC, the conductivity of baseflow; ROC, the conductivity of surface runoff) and of the two variables (yk, the specific streamflow; Qck, the specific conductivity of streamflow), and then estimates the uncertainty of BFI. The analysis shows that when the time series is longer than 365 days, the random measurement errors of yk or Qck will cancel each other, and the influence on BFI can be neglected. Dimensionless sensitivity indices (the ratio of the relative error of BFI to the relative error of BFC or ROC) can well express the propagation of errors or uncertainties of BFC or ROC into BFI. Based on the sensitivity analysis, the uncertainty estimation method of BFI is derived. Representative sensitivity indices and BFI' uncertainties are yielded by application of the resulting equations to 24 watersheds in the United States. The results indicate that BFI is more sensitive to BFC, and the conductivity two-component hydrograph separation method may be more suitable for the long time series in a small watershed. After considering the mutual offset of the measurement errors of conductivity and streamflow, the uncertainty of BFI is reduced by half.

scholarly journals Parameter Optimization, Uncertainty Estimation and Sensitivity Analysis in Hydrological Modeling

2018 ◽  
Vol 3 (11) ◽  
pp. 66-72
Author(s):  
Rajesh VijayKumar Kherde ◽  
Priyadarshi H. Sawant
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Parameter Optimization ◽  
Optimum Parameter ◽  
Performance Measure ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Local Optimum ◽  
Local Optima

This paper describes the application of Monte-Carlo simulations for parameter optimization, uncertainty estimation and sensitivity analysis using hydrological model developed by author [8] for Wardha River basin, Maharashtra, India. The Monte Carlo simulations revealed that the average values of parameters for the local optima of the calibration period seem to give good fit to the data and performance measure (NSE) does not differ significantly from the local optima of the respective calibration years. It is interesting to notice that, if the Monte Carlo simulations are carried out all over again, it generate yet another set of random numbers as realizations of model parameters. However the model objective function (NSE) differs mere by 0.1% by running the new set of realizations and the local optimum parameter values are close to the earlier local optima. It seems that the model structure is in agreement with the ‘‘equifinality’’ or ‘‘non-uniqueness’’ concept as many different parameter sets give good fit to the data. However particular area of the parameter space is observed to be dominant in fitting the available observations, this is in contradiction to Beven’s theory behind rejecting the idea of optimum parameter set.

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