A reliable deep ensemble hybrid model for urban air quality health index forecasting in maritime Canada
Article
| Article Title | A reliable deep ensemble hybrid model for urban air quality health index forecasting in maritime Canada |
|---|---|
| ERA Journal ID | 4673 |
| Article Category | Article |
| Authors | Jamei, Mehdi, Randhawa, Gurjit S., Ali, Mumtaz, Karbasi, Masoud, Olumegbon, Ismail, Cheema, Saad Javed, Esau, Travis J., Zaman, Qamar U. and Farooque, Aitazaz A. |
| Journal Title | Environmental Modelling and Software |
| Journal Citation | 197 |
| Article Number | 106837 |
| Number of Pages | 22 |
| Year | 2026 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 1364-8152 |
| 1873-6726 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.envsoft.2025.106837 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1364815225005213 |
| Abstract | Accurate Air Quality Health Index (AQHI) forecasting is crucial for safeguarding public health and informing policy decisions in coastal urban regions of Maritime Canada. This study introduces a graph-enhanced deep ensemble model that integrates Robust Empirical Mode Decomposition (REMD), Deep Ensemble Random Vector Functional Link (DeepERVFL), graph-based feature selection, and Borda Count multi-criteria decision making for multi-weekly AQHI forecasting. Forecast uncertainty is quantified using bootstrap resampling to ensure confidence in the results. Benchmarking against Recursive LSTM and Histogram-Based Gradient Boosting Ensemble (HBGBE) models shows the superior performance of the REMD-DeepERVFL framework, with BORDA scores of 0.940 (T+1) and 1.06 (T+3) in Halifax, 0.797 (T+3) in Charlottetown, and 0.931 (T+3) in St. John's. The framework supports air-quality early warning systems, public health communication, and climate-health monitoring, offering timely and reliable information. This hybrid approach provides a robust, scalable, and uncertainty-aware solution for regional AQHI forecasting in Atlantic Canada. |
| Keywords | Air quality health index (AQHI); Forecasting; Deep ensemble random vector functional link (DeepERVFL); Maritime Canada; Graph-based feature selection |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461103. Deep learning |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of Prince Edward Island, Canada |
| Shahid Chamran University of Ahvaz, Iran | |
| University of Guelph, Canada | |
| School of Business, Law, Humanities and Pathways - Business | |
| University of Maryland, United States | |
| Dalhousie University, Canada |
https://research.usq.edu.au/item/100x8v/a-reliable-deep-ensemble-hybrid-model-for-urban-air-quality-health-index-forecasting-in-maritime-canada
29
total views1
total downloads9
views this month0
downloads this month