Computational Statistics & Data Analysis
Sensitivity analysis of model output: an investigation of new techniques
Computational Statistics & Data Analysis
Sensitivity analysis of model output: performance of the iterated fractional factorial design method
Computational Statistics & Data Analysis
Testing the PEARL model in the Netherlands and Sweden
Environmental Modelling & Software
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
GUI-HDMR - A software tool for global sensitivity analysis of complex models
Environmental Modelling & Software
Modelling the sensitivity to various factors of shipborne pollutant discharges
Environmental Modelling & Software
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Global sensitivity analysis in the development of first principle-based eutrophication models
Environmental Modelling & Software
Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
An efficient integrated approach for global sensitivity analysis of hydrological model parameters
Environmental Modelling & Software
The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making
Environmental Modelling & Software
Environmental Modelling & Software
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Sensitivity analysis methods based on multiple simulations such as Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) are very efficient, especially for complex computer models. The application of these methods involves successive runs of the model under investigation with different sampled sets of the uncertain model-input variables and (or) parameters. The subsequent statistical analysis based on regression and correlation analysis among the input variables and model output allows determination of the input variables or the parameters to which the model prediction uncertainty is most sensitive. The sensitivity effect of the model-input variables or parameters on the model outputs can be quantified by various statistical measures based on regression and correlation analysis. This paper provides a thorough review of these measures and their properties and develops a concept for selecting the most robust and reliable measures for practical use. The concept is demonstrated through the application of Latin Hypercube Sampling as the sensitivity analysis technique to the DUFLOW water-quality model developed for the Dender River in Belgium. The results obtained indicate that the Semi-Partial Correlation Coefficient and its rank equivalent the Semi-Partial Rank Correlation Coefficient can be considered adequate measures to assess the sensitivity of the DUFLOW model to the uncertainty in its input parameters.