Venue & metadata

• Journal/Proceedings: Proceedings - European Council for Modelling and Simulation, ECMS
• Volume: 38
• Number: 1
• Pages: 558 – 564
• Note: Cited by: 2
• Author keywords: Carbon monoxide; Environmental Monitoring; eX-plainalble Artificial Intelligence; Machine Learning; Pollution

Abstract

Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

Keywords

Learning systems GS Machine learning GS Campania GS CO concentrations GS Ensemble models GS Environmental conditions GS Environmental Monitoring GS Ex-plainalble artificial intelligence GS Informed decision GS Machine-learning GS Non-linear phenomenon GS Pollution dynamics GS Carbon monoxide GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Campanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558