Complementary ensemble empirical mode decomposition and independent recurrent neural network model for predicting air quality index

Abstract

Establishing a scientific and effective air quality prediction model is of great scientific value and practical significance for protecting people’s health and promoting social harmony and stability. However, existing prediction models have certain shortcomings in various aspects. To address these shortcomings, this paper combines different methods to achieve better prediction accuracy. Outlier analysis is done for air quality index (AQI) using an isolation forest algorithm. An air quality prediction system that consists of data preprocessing, optimization, prediction, and modification is established. The complementary ensemble empirical mode decomposition (CEEMD), modified particle swarm optimization (MPSO) algorithm, independent recurrent neural network (IndRNN), and nonlinear correction strategy are employed for the prediction. We select five cities with different AQI as the experiment sites, and three experiments are designed to test the accuracy of the prediction model. The results reveal that (1) by using CEEMD data decomposition technology to deal with the non-stationarity and nonlinearity of the original data, the prediction accuracy of the original cyclic neural network model can be improved by about 15%. (2) The prediction system with the CEEMD-MPSO-IndRNN model as the core prediction module and with a nonlinear error correction strategy has good scalability and robustness for air quality prediction. (3) The performance of the air quality prediction model constructed with systematic thought is better than other comparable models, and it can effectively predict the time series data of different cities and frequencies.