A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction
Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this stu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pak, Unjin [verfasserIn] Kim, Chungsong [verfasserIn] Ryu, Unsok [verfasserIn] Sok, Kyongjin [verfasserIn] Pak, Sungnam [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Air quality, atmosphere and health - Dordrecht : Springer Netherlands, 2008, 11(2018), 8 vom: 16. Aug., Seite 883-895 |
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| Übergeordnetes Werk: |
volume:11 ; year:2018 ; number:8 ; day:16 ; month:08 ; pages:883-895 |
| Links: |
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| DOI / URN: |
10.1007/s11869-018-0585-1 |
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| Katalog-ID: |
SPR023477229 |
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| 245 | 1 | 2 | |a A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
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| 520 | |a Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. | ||
| 650 | 4 | |a Ozone prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CNN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a LSTM |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CNN-LSTM |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kim, Chungsong |e verfasserin |4 aut | |
| 700 | 1 | |a Ryu, Unsok |e verfasserin |4 aut | |
| 700 | 1 | |a Sok, Kyongjin |e verfasserin |4 aut | |
| 700 | 1 | |a Pak, Sungnam |e verfasserin |4 aut | |
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10.1007/s11869-018-0585-1 doi (DE-627)SPR023477229 (SPR)s11869-018-0585-1-e DE-627 ger DE-627 rakwb eng 690 ASE 43.11 bkl 44.13 bkl Pak, Unjin verfasserin aut A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Kim, Chungsong verfasserin aut Ryu, Unsok verfasserin aut Sok, Kyongjin verfasserin aut Pak, Sungnam verfasserin aut Enthalten in Air quality, atmosphere and health Dordrecht : Springer Netherlands, 2008 11(2018), 8 vom: 16. Aug., Seite 883-895 (DE-627)565516515 (DE-600)2424084-9 1873-9326 nnns volume:11 year:2018 number:8 day:16 month:08 pages:883-895 https://dx.doi.org/10.1007/s11869-018-0585-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.11 ASE 44.13 ASE AR 11 2018 8 16 08 883-895 |
| spelling |
10.1007/s11869-018-0585-1 doi (DE-627)SPR023477229 (SPR)s11869-018-0585-1-e DE-627 ger DE-627 rakwb eng 690 ASE 43.11 bkl 44.13 bkl Pak, Unjin verfasserin aut A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Kim, Chungsong verfasserin aut Ryu, Unsok verfasserin aut Sok, Kyongjin verfasserin aut Pak, Sungnam verfasserin aut Enthalten in Air quality, atmosphere and health Dordrecht : Springer Netherlands, 2008 11(2018), 8 vom: 16. Aug., Seite 883-895 (DE-627)565516515 (DE-600)2424084-9 1873-9326 nnns volume:11 year:2018 number:8 day:16 month:08 pages:883-895 https://dx.doi.org/10.1007/s11869-018-0585-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.11 ASE 44.13 ASE AR 11 2018 8 16 08 883-895 |
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10.1007/s11869-018-0585-1 doi (DE-627)SPR023477229 (SPR)s11869-018-0585-1-e DE-627 ger DE-627 rakwb eng 690 ASE 43.11 bkl 44.13 bkl Pak, Unjin verfasserin aut A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Kim, Chungsong verfasserin aut Ryu, Unsok verfasserin aut Sok, Kyongjin verfasserin aut Pak, Sungnam verfasserin aut Enthalten in Air quality, atmosphere and health Dordrecht : Springer Netherlands, 2008 11(2018), 8 vom: 16. Aug., Seite 883-895 (DE-627)565516515 (DE-600)2424084-9 1873-9326 nnns volume:11 year:2018 number:8 day:16 month:08 pages:883-895 https://dx.doi.org/10.1007/s11869-018-0585-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.11 ASE 44.13 ASE AR 11 2018 8 16 08 883-895 |
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10.1007/s11869-018-0585-1 doi (DE-627)SPR023477229 (SPR)s11869-018-0585-1-e DE-627 ger DE-627 rakwb eng 690 ASE 43.11 bkl 44.13 bkl Pak, Unjin verfasserin aut A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Kim, Chungsong verfasserin aut Ryu, Unsok verfasserin aut Sok, Kyongjin verfasserin aut Pak, Sungnam verfasserin aut Enthalten in Air quality, atmosphere and health Dordrecht : Springer Netherlands, 2008 11(2018), 8 vom: 16. Aug., Seite 883-895 (DE-627)565516515 (DE-600)2424084-9 1873-9326 nnns volume:11 year:2018 number:8 day:16 month:08 pages:883-895 https://dx.doi.org/10.1007/s11869-018-0585-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.11 ASE 44.13 ASE AR 11 2018 8 16 08 883-895 |
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10.1007/s11869-018-0585-1 doi (DE-627)SPR023477229 (SPR)s11869-018-0585-1-e DE-627 ger DE-627 rakwb eng 690 ASE 43.11 bkl 44.13 bkl Pak, Unjin verfasserin aut A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Kim, Chungsong verfasserin aut Ryu, Unsok verfasserin aut Sok, Kyongjin verfasserin aut Pak, Sungnam verfasserin aut Enthalten in Air quality, atmosphere and health Dordrecht : Springer Netherlands, 2008 11(2018), 8 vom: 16. Aug., Seite 883-895 (DE-627)565516515 (DE-600)2424084-9 1873-9326 nnns volume:11 year:2018 number:8 day:16 month:08 pages:883-895 https://dx.doi.org/10.1007/s11869-018-0585-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.11 ASE 44.13 ASE AR 11 2018 8 16 08 883-895 |
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Enthalten in Air quality, atmosphere and health 11(2018), 8 vom: 16. Aug., Seite 883-895 volume:11 year:2018 number:8 day:16 month:08 pages:883-895 |
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Enthalten in Air quality, atmosphere and health 11(2018), 8 vom: 16. Aug., Seite 883-895 volume:11 year:2018 number:8 day:16 month:08 pages:883-895 |
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Ozone prediction Deep learning CNN LSTM CNN-LSTM |
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Air quality, atmosphere and health |
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Pak, Unjin @@aut@@ Kim, Chungsong @@aut@@ Ryu, Unsok @@aut@@ Sok, Kyongjin @@aut@@ Pak, Sungnam @@aut@@ |
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2018-08-16T00:00:00Z |
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In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ozone prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LSTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN-LSTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Chungsong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ryu, Unsok</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sok, Kyongjin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pak, Sungnam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Air quality, atmosphere and health</subfield><subfield code="d">Dordrecht : Springer Netherlands, 2008</subfield><subfield code="g">11(2018), 8 vom: 16. Aug., Seite 883-895</subfield><subfield code="w">(DE-627)565516515</subfield><subfield code="w">(DE-600)2424084-9</subfield><subfield code="x">1873-9326</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:8</subfield><subfield code="g">day:16</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:883-895</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11869-018-0585-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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Pak, Unjin |
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Pak, Unjin ddc 690 bkl 43.11 bkl 44.13 misc Ozone prediction misc Deep learning misc CNN misc LSTM misc CNN-LSTM A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
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690 ASE 43.11 bkl 44.13 bkl A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction Ozone prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 |
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ddc 690 bkl 43.11 bkl 44.13 misc Ozone prediction misc Deep learning misc CNN misc LSTM misc CNN-LSTM |
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A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
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Pak, Unjin Kim, Chungsong Ryu, Unsok Sok, Kyongjin Pak, Sungnam |
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hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
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A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
| abstract |
Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. |
| abstractGer |
Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. |
| abstract_unstemmed |
Abstract Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models. |
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| container_issue |
8 |
| title_short |
A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction |
| url |
https://dx.doi.org/10.1007/s11869-018-0585-1 |
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| author2 |
Kim, Chungsong Ryu, Unsok Sok, Kyongjin Pak, Sungnam |
| author2Str |
Kim, Chungsong Ryu, Unsok Sok, Kyongjin Pak, Sungnam |
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| doi_str |
10.1007/s11869-018-0585-1 |
| up_date |
2024-07-03T19:10:42.963Z |
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| score |
7.397564 |