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A novel combined model based on echo state network – a case study of PM10 and PM2.5 prediction in China.
- Source :
- Environmental Technology; Jun2020, Vol. 41 Issue 15, p1937-1949, 13p
- Publication Year :
- 2020
-
Abstract
- Particulate Matters such as PM<subscript>10</subscript>, PM<subscript>2.5</subscript> 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 PM<subscript>10</subscript> and PM<subscript>2.5</subscript>. 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 SO<subscript>2</subscript>, NO, NO<subscript>2</subscript>, O<subscript>3</subscript> 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09593330
- Volume :
- 41
- Issue :
- 15
- Database :
- Complementary Index
- Journal :
- Environmental Technology
- Publication Type :
- Academic Journal
- Accession number :
- 143611449
- Full Text :
- https://doi.org/10.1080/09593330.2018.1551941