An air quality forecasting model based on improved convnet and RNN
Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction o...
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| Autor*in: |
Wang, Baowei [verfasserIn] Kong, Weiwen [verfasserIn] Zhao, Peng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2021), 14 vom: 10. Mai, Seite 9209-9218 |
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volume:25 ; year:2021 ; number:14 ; day:10 ; month:05 ; pages:9209-9218 |
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10.1007/s00500-021-05843-w |
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| 520 | |a Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. | ||
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| 700 | 1 | |a Zhao, Peng |e verfasserin |4 aut | |
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10.1007/s00500-021-05843-w doi (DE-627)SPR044334249 (SPR)s00500-021-05843-w-e DE-627 ger DE-627 rakwb eng Wang, Baowei verfasserin aut An air quality forecasting model based on improved convnet and RNN 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. Air pollution forecasting (dpeaa)DE-He213 Recurrent neural network (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Bidirectional gated recurrent unit (dpeaa)DE-He213 Dense (dpeaa)DE-He213 Kong, Weiwen verfasserin aut Zhao, Peng verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 10. Mai, Seite 9209-9218 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:10 month:05 pages:9209-9218 https://dx.doi.org/10.1007/s00500-021-05843-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 10 05 9209-9218 |
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10.1007/s00500-021-05843-w doi (DE-627)SPR044334249 (SPR)s00500-021-05843-w-e DE-627 ger DE-627 rakwb eng Wang, Baowei verfasserin aut An air quality forecasting model based on improved convnet and RNN 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. Air pollution forecasting (dpeaa)DE-He213 Recurrent neural network (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Bidirectional gated recurrent unit (dpeaa)DE-He213 Dense (dpeaa)DE-He213 Kong, Weiwen verfasserin aut Zhao, Peng verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 10. Mai, Seite 9209-9218 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:10 month:05 pages:9209-9218 https://dx.doi.org/10.1007/s00500-021-05843-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 10 05 9209-9218 |
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10.1007/s00500-021-05843-w doi (DE-627)SPR044334249 (SPR)s00500-021-05843-w-e DE-627 ger DE-627 rakwb eng Wang, Baowei verfasserin aut An air quality forecasting model based on improved convnet and RNN 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. Air pollution forecasting (dpeaa)DE-He213 Recurrent neural network (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Bidirectional gated recurrent unit (dpeaa)DE-He213 Dense (dpeaa)DE-He213 Kong, Weiwen verfasserin aut Zhao, Peng verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 10. Mai, Seite 9209-9218 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:10 month:05 pages:9209-9218 https://dx.doi.org/10.1007/s00500-021-05843-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 10 05 9209-9218 |
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10.1007/s00500-021-05843-w doi (DE-627)SPR044334249 (SPR)s00500-021-05843-w-e DE-627 ger DE-627 rakwb eng Wang, Baowei verfasserin aut An air quality forecasting model based on improved convnet and RNN 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. Air pollution forecasting (dpeaa)DE-He213 Recurrent neural network (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Bidirectional gated recurrent unit (dpeaa)DE-He213 Dense (dpeaa)DE-He213 Kong, Weiwen verfasserin aut Zhao, Peng verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 10. Mai, Seite 9209-9218 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:10 month:05 pages:9209-9218 https://dx.doi.org/10.1007/s00500-021-05843-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 10 05 9209-9218 |
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10.1007/s00500-021-05843-w doi (DE-627)SPR044334249 (SPR)s00500-021-05843-w-e DE-627 ger DE-627 rakwb eng Wang, Baowei verfasserin aut An air quality forecasting model based on improved convnet and RNN 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. Air pollution forecasting (dpeaa)DE-He213 Recurrent neural network (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Bidirectional gated recurrent unit (dpeaa)DE-He213 Dense (dpeaa)DE-He213 Kong, Weiwen verfasserin aut Zhao, Peng verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 10. Mai, Seite 9209-9218 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:10 month:05 pages:9209-9218 https://dx.doi.org/10.1007/s00500-021-05843-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 10 05 9209-9218 |
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Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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An air quality forecasting model based on improved convnet and RNN |
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Kong, Weiwen Zhao, Peng |
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2024-07-04T00:09:53.504Z |
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