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Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition
A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatter...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031189/ https://www.ncbi.nlm.nih.gov/pubmed/36949097 http://dx.doi.org/10.1038/s41598-023-31569-w |
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author | Li, Wenlin Jiang, Xuchu |
author_facet | Li, Wenlin Jiang, Xuchu |
author_sort | Li, Wenlin |
collection | PubMed |
description | A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM(2.5), PM(10), SO(2), NO(2), CO, and O(3) data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively. |
format | Online Article Text |
id | pubmed-10031189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100311892023-03-22 Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition Li, Wenlin Jiang, Xuchu Sci Rep Article A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM(2.5), PM(10), SO(2), NO(2), CO, and O(3) data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively. Nature Publishing Group UK 2023-03-22 /pmc/articles/PMC10031189/ /pubmed/36949097 http://dx.doi.org/10.1038/s41598-023-31569-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Wenlin Jiang, Xuchu Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title | Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title_full | Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title_fullStr | Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title_full_unstemmed | Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title_short | Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition |
title_sort | prediction of air pollutant concentrations based on tcn-bilstm-dmattention with stl decomposition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031189/ https://www.ncbi.nlm.nih.gov/pubmed/36949097 http://dx.doi.org/10.1038/s41598-023-31569-w |
work_keys_str_mv | AT liwenlin predictionofairpollutantconcentrationsbasedontcnbilstmdmattentionwithstldecomposition AT jiangxuchu predictionofairpollutantconcentrationsbasedontcnbilstmdmattentionwithstldecomposition |