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231225s2020 xx |||||o 00| ||eng c |
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7 |
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|a 10.1080/09593330.2018.1551941
|2 doi
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|a pubmed24n0970.xml
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|a (DE-627)NLM291043100
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|a (NLM)30472931
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhang, Hairui
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A novel combined model based on echo state network - a case study of PM10 and PM2.5 prediction in China
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 22.05.2020
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|a Date Revised 22.05.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM10 and PM2.5. In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO2, NO, NO2, O3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a ESN
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| 650 |
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4 |
|a Elman
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| 650 |
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4 |
|a PM10 and PM2.5
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| 650 |
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4 |
|a PSO
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| 650 |
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4 |
|a SACBP
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a neural network model
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| 650 |
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7 |
|a Air Pollutants
|2 NLM
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| 650 |
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|a Metals, Heavy
|2 NLM
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| 650 |
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|a Particulate Matter
|2 NLM
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| 700 |
1 |
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|a Shang, Zhihao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song, Yanru
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Zhaoshuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Lian
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Environmental technology
|d 1993
|g 41(2020), 15 vom: 12. Juni, Seite 1937-1949
|w (DE-627)NLM098202545
|x 1479-487X
|7 nnns
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| 773 |
1 |
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|g volume:41
|g year:2020
|g number:15
|g day:12
|g month:06
|g pages:1937-1949
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| 856 |
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|u http://dx.doi.org/10.1080/09593330.2018.1551941
|3 Volltext
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