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- W3213369675 abstract "Carbon monoxide (CO) is one of the dangerous air pollutants due to its negative impact on human health. Therefore, accurate forecasting of CO concentration is essential to control air pollution. This study aims to forecast the concentration of CO using sequences to sequence models namely convolutional neural network and long short-term memory (CNN-LSTM) and sequence to sequence LSTM (seq2seq LSTM). The proposed forecasting models are validated using hourly air quality datasets from six monitoring stations in Selangor to forecast CO concentration at 1 h to 6 h ahead of the time horizon. The performances of proposed models are evaluated in terms of statistical equations namely root mean square error (RMSE), mean square error (MAE) and mean percentage error (MAPE). CNN-LSTM and seq2seq LSTM model excellently forecast air pollutant concentration for 6 h ahead with RMSE of 0.2899 and 0.2215, respectively. Additionally, it is found that seq2seq LSTM has slightly improved CNN-LSTM indicates the effectiveness of the architecture in the forecasting. However, both proposed architectures illustrate promising results and are reliable in the forecasting of CO concentration." @default.
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- W3213369675 date "2021-01-01" @default.
- W3213369675 modified "2023-09-27" @default.
- W3213369675 title "Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach" @default.
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- W3213369675 doi "https://doi.org/10.1007/978-3-030-90235-3_45" @default.
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