Sensitivity analysis: A discipline coming of age. (December 2021)

• Record Type: Journal Article
• Authors: Saltelli, Andrea; Jakeman, Anthony; Razavi, Saman; Wu, Qiongli
• Is Part Of: Environmental modelling & software. Volume 146(2021)
• Publication Date: 2021-12
• Subjects: Sensitivity analysis -- Uncertainty analysis -- Evidence based policy -- Machine learning -- Validation and verification of mathematical models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105226
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Sensitivity analysis (SA) as a 'formal' and 'standard' component of scientific development and policy support is relatively young. Many researchers and practitioners from a wide range of disciplines have contributed to SA over the last three decades, and the SAMO (sensitivity analysis of model output) conferences, since 1995, have been the primary driver of breeding a community culture in this heterogeneous population. Now, SA is evolving into a mature and independent field of science, indeed a discipline with emerging applications extending well into new areas such as data science and machine learning. At this growth stage, the present editorial leads a special issue consisting of one Position Paper on " The future of sensitivity analysis " and 11 research papers on " Sensitivity analysis for environmental modelling " published in Environmental Modelling & Software in 2020–21. Highlights: Advances of science and policy has deep but informal roots in sensitivity analysis. Modern sensitivity analysis is now evolving into a formal and independent discipline. New areas such data science and machine learning benefit from sensitivity analysis. Challenges, methodological progress, and outlook are outlined in this special issue.