A novel combined model based on echo state network – a case study of PM10 and PM2.5 prediction in China. Issue 15 (2nd July 2020)

• Record Type: Journal Article
• Authors: Zhang, Hairui; Shang, Zhihao; Song, Yanru; He, Zhaoshuang; Li, Lian
• Is Part Of: Environmental technology. Volume 41:Issue 15(2020)
• Page Start: 1937
• Page End: 1949
• Publication Date: 2020-07-02
• Subjects: PM10 and PM2.5 -- machine learning -- neural network model -- Elman -- PSO -- ESN -- SACBP; Environmental engineering -- Periodicals; Environmental protection -- Periodicals; 628.05
• DOI: 10.1080/09593330.2018.1551941
• Languages: English
• ISSNs: 0959-3330

Abstract

ABSTRACT: 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. GRAPHICAL ABSTRACT: