Screening, predicting, and computer experiments
Technometrics
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Algebraic sensitivity analysis of environmental models
Environmental Modelling & Software
GUI-HDMR - A software tool for global sensitivity analysis of complex models
Environmental Modelling & Software
Derivative based global sensitivity measures and their link with global sensitivity indices
Mathematics and Computers in Simulation
Management Option Rank Equivalence (MORE) - A new method of sensitivity analysis for decision-making
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Sobol' sensitivity analysis of a complex environmental model
Environmental Modelling & Software
Environmental Modelling & Software
A long-term sensitivity analysis of the denitrification and decomposition model
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Application of a combined sensitivity analysis approach on a pesticide environmental risk indicator
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
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Decision and policy-makers benefit from the utilization of computer codes in an increasing number of areas and applications. Several authorities and agencies recommend the utilization of proper sensitivity analysis methods in order to confidently entrust model results. In this respect, density-based techniques have recently attracted interest among academicians and practitioners, for their property to characterize uncertainty in terms of the entire distribution of an output variable. However, their estimation is a challenging task and, without a proper methodical approach, errors in the estimates can lead to misleading conclusions. In this work, we propose sampling plans for reducing the computational burden of sensitivity estimates while improving and controlling the accuracy in the estimation. We compare designs based on column substitutions and designs based on permutations. We investigate their behaviour in terms of type I and type II errors. We apply the methods to the Level E model, a computational tool developed by the Nuclear Energy Agency of the OECD for the assessment of nuclear waste disposal sites. Results show that application of the proposed sampling plans allows one to obtain confidence in the sensitivity estimates at a number of model runs several orders of magnitude lower than a brute-force approach. This assessment, based upon the entire distribution of the model output, provides us with ways to effectively reduce uncertainty in the model output, either by prioritizing the model factors that need to be better known or by prioritizing the areas where additional modelling efforts are needed.