Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield – A case study

King, David Michael; Perera, B. J. C

doi:10.1016/j.jhydrol.2012.10.017

Cited by 101 publications

(61 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, Global SA (GSA) studies the effects of input variations on outputs within the entire allowable ranges of input space. GSA methods range from qualitative screening (Campolongo et al, 2007(Campolongo et al, , 2011Saltelli and Annoni, 2010) to quantitative techniques based on variance decomposition, in which the Fourier amplitude sensitivity test (FAST) (Cukier et al, 1973), response surface methodology (RSM) (McKay et al, 1979), Morris screening method (Morris, 1991;Ruano et al, 2012;Touhami et al, 2013), and Sobol' method (Sobol', 1993) are the most widely investigated DeJonge et al, 2012;Zheng et al, 2012;Zhan and Zhang, 2013;Gamerith et al, 2013;King and Perera, 2013). Shin et al (2013) used the Morris and Sobol methods to analyze model parameter sensitivities and pointed out that the result of applying the Morris method is similar to that of Sobol but there is a slight difference in the ordering of the parameter sensitivities.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

View full text Add to dashboard Cite

a b s t r a c tWe present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.

show abstract

Section: Sensitivity Analysismentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

View full text Add to dashboard Cite

show abstract

“…Subsequently, some improvements have been made by others authors [17][18][19], but basically the main idea is to estimate the sensitivity indices in the frequency domain by using Fourier series expansion. Some important studies were focused on how to apply GSA in order to assess correctly the importance of input variables or to evaluate the robustness of a mathematical model [20,21].…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Homogeneous Sono-Fenton Process: Statistical Modeling and Global Sensitivity Analysis

Căilean

Ungureanu

Teodosiu

2015

International Journal of Nonlinear Sciences and Numerical Simulation

View full text Add to dashboard Cite

The main objective of this study is to obtain and validate a mathematical model to describe a complex homogeneous Sono-Fenton (HSF) process used for the removal of 4-chlorophenol model pollutant from aqueous effluents. The investigated process parameters (acoustic amplitude, power density depending on the surface of the tip, initial pollutant concentration and time) serve as input parameters for the statistical modeling, while the output parameters considered are the final pollutant concentration and energy delivered to the sample. The accuracy of the models is analyzed by the values of the determination coefficients and by graphical tools available in MATLAB software such as: the KolmogorovSmirnov test (KS test), the graphical sensitivity tools, e.g. contribution to the sample mean (CSM) and variance (CSV) plots. The robustness of the model is also analyzed by global sensitivity analysis. Furthermore, the optimum set of operating conditions are determined by using the nlintool function.

show abstract

“…Identifying and ranking important uncertain parameters is therefore essential for efficient improvement of the accuracy of DSSE. Sensitivity analysis (SA) techniques, which are able to provide a framework to rank and identify the most influential uncertain parameters, have been widely used to determine how input variability propagates through a computational model to its output result [7]. In [8], nine SA techniques including probabilistic approaches have been compared in terms of their performance and efficiency, and it has been demonstrated that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Influence of uncertainties and parameter structural dependencies in distribution system state estimation

Liao

Milanović

Hasan

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

This paper evaluates a number of uncertain parameters that affect the accuracy of distribution system state estimation, and ranks their importance using an efficient sensitivity analysis technique, Morris screening method. The influence of the uncertain parameters on state estimation performance is analysed globally and zonally. Furthermore the dependence structure between the critical variable and state estimation accuracy is analysed using copula to establish their relationship at different section of the bivariate space. The sensitivity of the critical parameter at different ranges is also studied and ranked using Morris screening methods to present the variation of state estimation performance when the critical variable is allocated at different sections within the feasible range. Accurate assessment of the importance of various uncertain parameters and the analysis of the dependence structure can inform power system operators which parameters will require the greatest levels of mitigation or increased monitoring accuracy in order to have satisfactory performance of distribution system state estimation.

show abstract

Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield – A case study

Cited by 101 publications

References 31 publications

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Homogeneous Sono-Fenton Process: Statistical Modeling and Global Sensitivity Analysis

Influence of uncertainties and parameter structural dependencies in distribution system state estimation

Contact Info

Product

Resources

About