Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis. (March 2022)

• Record Type: Journal Article
• Authors: Li, Yanzhao; Guo, Ju-e; Sun, Shaolong; Li, Jianing; Wang, Shouyang; Zhang, Chengyuan
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Air quality forecasting -- Artificial intelligence -- Machine learning -- Scientometrics -- Content 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.2022.105329
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Artificial intelligence (AI) techniques have substantially changed the research paradigm in the field of air quality forecasting due to their powerful performance. Considering the improvement in the availability of air quality data and the rapid proliferation of AI techniques, it is necessary to comprehensively and quantitatively review the development of air quality forecasting with AI techniques during the last two decades (2000–2019) by scientometric and content analysis. First, an overview of the relevant countries, institutions, authors, journals, and papers is presented. Then, the research hotspots and frontier evolution are explored by adopting reference co-citation analysis and keyword co-occurrence analysis. Furthermore, this study conducts a content analysis to investigate current topical interests to identify research gaps and propose future research directions. The analytical framework and the findings provide helpful insights into the prospects in air quality forecasting with AI techniques. Highlights: Air quality forecasting with AI is investigated deeply and comprehensively. A top-down review integrating scientometric and content analysis is adopted. Various statistics and visualizations of this research field are provided. The research hotspots and frontier evolution in this research field are explored.