Olumide Adenuga
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Olumide Adenuga has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Adegboyega Sotunbo
Department of Building, University of Lagos, Lagos 101017, Nigeria
1 shared publication - Majeed Olaide Oladokun
School of Science, Engineering and Environment, University of Salford, Salford M5 4NT, UK
1 shared publication - Nwabueze Emekwuru
Department of Engineering, Harper Adams University, Newport TF10 8NB, UK
1 shared publication - Obuks Ejohwomu
Department of Civil Engineering and Management, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom; CIDB Centre of Excellence, University of Johannesburg, Johannesburg, 2092, South Africa
1 shared publication - Olakekan Shamsideen Oshodi
Anglia Ruskin University
1 shared publication - Oyegoke Teslim Bukoye
Information, Decisions and Operations (IDO) Division, School of Management University of Bath Bath UK
1 shared publication
Latest Publications
- Open Access
Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods
Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.
Open AccessModelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods
Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.