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- W4285606318 abstract "Multi-step ahead pollution forecasting has an essential role in mitigating health risks. Multi-Input and Multi-Output (MIMO) multi-step forecasting is valuable, especially for long-term pollution forecasting. Multi-output pollution forecasting results help comprehend the overall idea of a particular location’s air quality level for a better environmental decision-making process. The traditional single output forecasting techniques include each step’s errors and ignore its prediction values for multi-step ahead forecasting. The proposed Multi-Output Long Short-Term Memory Auto-Encoder (M-LSTMA) with the MIMO approach accumulates each step prediction value to conduct multi-step forecasting in light of this challenge. In addition to that, the proposed model utilized a multiple imputation approach to handle the missing values of the dataset and employed both the climate and spatial attributes to improve forecasting results. Overall, the results demonstrate that the M-LSTMA model improves forecasting accuracy more significantly than the baseline models, proving its efficiency for long-term Spatio-temporal forecasting." @default.
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- W4285606318 date "2022-09-01" @default.
- W4285606318 modified "2023-09-30" @default.
- W4285606318 title "Multi-output Spatio-temporal air pollution forecasting using neural network approach" @default.
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- W4285606318 doi "https://doi.org/10.1016/j.asoc.2022.109316" @default.
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