Model validation: A bibliometric analysis of the literature. (July 2019)

• Record Type: Journal Article
• Authors: Eker, Sibel; Rovenskaya, Elena; Langan, Simon; Obersteiner, Michael
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 43
• Page End: 54
• Publication Date: 2019-07
• Subjects: Model validation -- Model evaluation -- Model testing -- Citation analysis -- Text-mining analysis; 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.2019.03.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Validation is a crucial step in environmental and economic modeling that establishes the reliability of models to be used in decision-making contexts. It is often said that validation approaches proposed in the literature are not widely adopted, and different modeling fields do not benefit from each other. This study analyses a broad academic literature on model validation, mainly in environmental and decision sciences, by using an innovative combination of bibliometric and text-mining tools. The results show that a data-driven validation practice is prevalent. Although most publications in the studied dataset resemble each other, the most-cited ones tend to be different from the rest in terms of their abstracts' content. Furthermore, the validation practices in different modeling areas are distinct, and do not extensively cite each other. In future, validation approaches can extend beyond data-oriented reliability for a wider acceptance of modeling in decision-making, and can synthesize the methods and views from various fields. Highlights: We conduct citation and text-mining analyses on a broad model validation literature. Data and predict are the most common words in the studied publication dataset. The most-cited publications are not similar to the rest in terms of their content. Validation practices of different modeling fields are closed to each other.