An effective screening design for sensitivity analysis of large models

By Francesca Campolongo, Jessica Cariboni, Andrea Saltelli. Environmental Modelling & Software, 2007

1,947 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7b0569980ca59e6b7d5c1f9dea97464640149b84

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In Computational Theory and Mathematics

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, methodology): “This method has been later revised and its sampling strategy improved (Campolongo et al., 2007).”
Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments (2012, methodology): “Campolongo et al. (2007) suggested that the Morris screening method is underutilized in the context of SA and can be used to simplify more robust methods such as Sobol’ (1993).”
Sensitivity analysis of infectious disease models: methods, advances and their application (2013, methodology): “the Morris method, also called the elementary effect method) [18,19], sampling-based methods (e.”
InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change (2016, methodology): “The screening step used an improved version of Morris’s elementary effects method (Morris, 1991; Campolongo et al., 2007).”
SALib: An open-source Python library for Sensitivity Analysis (2017, methodology): “(2010)), Morris (Morris 1991, Campolongo, Cariboni, and Saltelli (2007)), FAST (Cukier et al. 1973, A. Saltelli, Tarantola, and Chan (1999)), Delta Moment-Independent Measure (E.”
Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis (2013, methodology): “This study employed an enhanced Morris method (Campolongo et al., 2007).”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “The function morris includes the Morris method with improvements in sampling strategy and the calculation of μ* to avoid Type-II errors (Campolongo et al. 2007).”
Variance-based sensitivity analysis of a forest growth model (2012, methodology): “Due to the high computational requirement of the variance-based SA for parameter-rich models, we used a 2-stage evaluation strategy (Campolongo and Saltelli, 1997).”
Bayesian calibration of building energy models with large datasets (2017, methodology): “Using the r trajectories, the commonly used sensitive measures that includes the modified mean µ⇤ (Campolongo et al., 2007) and the standard deviation of each of the ith parameter can then be computed by: µ⇤ i = rX j=1 |d ij (✓)|/r (2.2) i = vuut rX j=1 (d ij (✓) µ i ) 2/r (2.3) where, r is the…”
Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification (2019, methodology): “To overcome a well-known short-coming of the calculation of the mean μi, in which elementary effects with opposite signs cancel one another, we report the mean of the absolute value of the elementary effects [6],”
Global sensitivity analysis for urban water quality modelling: terminology, convergence and comparison of different methods. (2015, methodology): “Due to the high complexity of environmental models, the spread of the GSA applications has been limited due to their high computational cost (Campolongo et al., 2007; Yang, 2011).”