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A Reliable Deep Ensemble Hybrid Model for Urban Air Quality Health Index Forecasting in Maritime Canada
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DOI：10.1016/j.envsoft.2025.106837.png)
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Abstract
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• A hybrid REMD-DeepERVFL framework is proposed for AQHI forecasting in Canada. • Graph-based feature selection integrated with BORDA MCDM enhances input ranking. • Soft sifting REMD effectively decomposes and denoises complex AQHI time series. • Bootstrap-based uncertainty quantification validates model reliability.
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