Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water-quality model parameters
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.
sensitivity analysis monte carlo simulation latin hypercube sampling correlation coefficients water quality modelling linear regression