Prediction of ground-level ozone by SOM-NARX hybrid neural network based on the correlation of predictors

Qinqing Xiong; Wenju Wang; Mingya Wang; Chunhui Zhang; Xuechun Zhang; Chun Chen; Mingshi Wang

doi:10.1016/j.isci.2022.105658

Prediction of ground-level ozone by SOM-NARX hybrid neural network based on the correlation of predictors

iScience. 2022 Nov 23;25(12):105658. doi: 10.1016/j.isci.2022.105658. eCollection 2022 Dec 22.

Authors

Qinqing Xiong¹, Wenju Wang¹, Mingya Wang¹, Chunhui Zhang¹, Xuechun Zhang¹, Chun Chen², Mingshi Wang¹

Affiliations

¹ College of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
² Henan Key Laboratory for Environmental Monitoring Technology, Zhengzhou 450004, China.

Abstract

Current approaches to ozone prediction using hybrid neural networks are numerous but not perfect. Decomposition algorithms ignore the correlation between predictors and ozone, and feature extraction methods rarely select appropriate predictors in terms of correlation, especially for VOCs. Therefore, this study proposes a hybrid neural network model SOM-NARX based on the correlation of predictors. The model is based on MIC to filter predictors, using SOM to make predictors as feature sequences and using NARX networks to make predictions. Data from the JCDZURI site were used for training, testing, and validation. The results show that the correlation of the predictors, classification numbers of SOM, neuron numbers, and delay steps can affect prediction accuracy. Model comparison shows that the SOM-NARX model has 13.82, 10.60, 6.58% and 12.05, 9.44, 68.14% RMSE, MAE, and MAEP in winter and summer, which is smaller than CNN-LSTM, CNN-BiLSTM, CNN-GRU, SOM-LSTM, SOM-BiLSTM, and SOM-GRU.

Keywords: Atmospheric science; Environmental monitoring; Remote sensing.