A novel combined model based on echo state network - a case study of PM 10 and PM 2.5 prediction in China.

Particulate Matters such as PM ₁₀ , PM _2.5 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 PM ₁₀ and PM _2.5 . 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 SO ₂ , NO, NO ₂ , O ₃ 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.