May 14, 2024, 4:42 a.m. | Wenhua Yu, Bahareh Nakisa, Seng W. Loke, Svetlana Stevanovic, Yuming Guo, Mohammad Naim Rastgoo

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07404v1 Announce Type: new
Abstract: Exposure to poor indoor air quality poses significant health risks, necessitating thorough assessment to mitigate associated dangers. This study aims to predict hourly indoor fine particulate matter (PM2.5) concentrations and investigate their correlation with outdoor PM2.5 levels across 24 distinct buildings in Australia. Indoor air quality data were gathered from 91 monitoring sensors in eight Australian cities spanning 2019 to 2022. Employing an innovative three-stage deep ensemble machine learning framework (DEML), comprising three base models …

abstract air pollution air quality arxiv assessment association australia correlation cs.ai cs.lg dangers data forecasting health matter modelling pollution quality risks sensor study type

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