Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach. (February 2022) - Wang, Qian; Guillaume, Joseph H.A.; Jakeman, John D.; Yang, Tao; Iwanaga, Takuya; Croke, Barry; Jakeman, Anthony J.
Abstract: Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol' G-function. Three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention. Highlights: Factor fixing can lead to substantial errors that have commonly been ignored. The adaptive framework investigates effects of factor fixing systematically. Morris and Sobol' sensitivity methods identify the same nine factors to fix. Errors caused by factor fixing are affected by default values to fix factors at.