Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy

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 systemsMachine learningCampaniaCO concentrationsEnsemble modelsEnvironmental conditionsEnvironmental MonitoringEx-plainalble artificial intelligenceInformed decisionMachine-learningNon-linear phenomenonPollution dynamicsCarbon monoxide

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

@conference{Campanile2024558,
  author = {Campanile, Lelio and Di Bonito, Luigi Piero and Natale, Francesco Di and Iacono, Mauro},
  title = {Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy},
  year = {2024},
  journal = {Proceedings - European Council for Modelling and Simulation, ECMS},
  volume = {38},
  number = {1},
  pages = {558 – 564},
  doi = {10.7148/2024-0558}
}

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