Sensitivity analysis of model output. Performance of the iterated fractional factorial design method

Saltelli, Andrea; Andres, Terry; Homma, Tetsuya

doi:10.1016/0167-9473(95)92843-m

Cited by 57 publications

(34 citation statements)

References 27 publications

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“…Using IFFD in ANOVA allows users to neglect higher order interactions not included in the model (Liang and Guo, 2003;Andres, 1997) while generating highly repeatable results (Saltelli et al, 1995). Consequently, the number of model runs required can be reduced substantially.…”

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

“…A detailed presentation of IFFD is beyond the scope of this paper. Readers interested in detailed descriptions of IFFD are referred to the following papers (Andres and Wayne, 1993;Andres, 1997;Saltelli et al, 1995).…”

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

“…IFFD extends the basic orthogonal fractional factorial design (FFD) by conducting multiple iterations. The basic operations in IFFD include orthogonalization, folding, replication and random sampling (Andres and Wayne, 1993;Andres, 1997;Saltelli et al, 1995). The orthogonalized design guarantees equal Y.…”

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

“…If one parameter or parameter interaction is analyzed at a time in succession, the total number of model runs will be excessively large and most hydrologic applications would be computationally intractable. In this study, the iterated fractional factorial design (IFFD) sampling scheme (Andres and Wayne, 1993;Andres, 1997;Saltelli et al, 1995) was used to limit the computational burden posed by ANOVA while seeking high quality results.…”

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

See 3 more Smart Citations

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Tang

Reed

Wagener

et al. 2007

Hydrol. Earth Syst. Sci.

312

220

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Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

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Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

Section: Analysis Of Variance Using Iterated Fractional Factorial Desmentioning

confidence: 99%

See 2 more Smart Citations

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Tang

Reed

Wagener

et al. 2007

Hydrol. Earth Syst. Sci.

312

220

View full text Add to dashboard Cite

show abstract

“…Scatter plots for all parameters and NSE coefficients of the four catchments were drawn using results of the MODAWEC-driven HBV-light application to perform sensitivity analysis, as suggested by [40,44,99]. Examples of two parameters for the studied basins are presented in Figure 9.…”

Section: Resultsmentioning

confidence: 99%

Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

Radchenko

Forkutsa

Frede

2014

Water

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Glaciers and snowmelt supply the Naryn and Karadarya rivers, and about 70% of the water available for the irrigated agriculture in the Ferghana Valley. Nineteen smaller catchments contribute the remaining water mainly from annual precipitation. The latter will gain importance if glaciers retreat as predicted. Hydrological models can visualize such climate change impacts on water resources. However, poor data availability often hampers simulating the contributions of smaller catchments. We tested several data pre-processing methods (gap filling, MODAWEC (MOnthly to DAily WEather Converter), lapse rate) and their effect on the performance of the HBV (Hydrologiska Byråns Vattenavdelning)-light model. Monte Carlo simulations were used to define parameter uncertainties and ensembles of behavioral model runs. Model performances were evaluated by constrained measures of goodness-of-fit criteria (cumulative bias, coefficient of determination, model efficiency coefficients (NSE) for high flow and log-transformed flow). The developed data pre-processing arrangement can utilize data of relatively poor quality (only monthly means or daily data with gaps) but still provide model results with NSE between 0.50 and 0.88. Some of these may not be accurate enough to directly guide water management applications. However, the pre-processing supports producing key information that may initiate rigging OPEN ACCESSWater 2014, 6 3271 of monitoring facilities, and enable water management to respond to fundamentally changing water availability.

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Sensitivity analysis of Repast computational ecology models with R/Repast

García

Rodríguez‐Patón

2016

Ecology and Evolution

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Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in‐silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

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Sensitivity analysis of model output. Performance of the iterated fractional factorial design method

Cited by 57 publications

References 27 publications

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

Sensitivity analysis of Repast computational ecology models with R/Repast

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