Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region. Issue 12 (28th October 2021)

• Record Type: Journal Article
• Authors: Li, Ping; Wang, Shengwei; Ji, Hao; Zhan, Yulin; Li, Honghong
• Is Part Of: Advanced theory and simulations. Volume 4:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-28
• Subjects: air pollution index -- bidirectional gated recurrent neural network -- improved adaptive dynamic particle swarm algorithm -- meteorological data -- particulate matter concentration; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100220
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Accurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural, the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution. Abstract : To accurately predict the air pollution index and solve the uncertain characteristics of the data. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm algorithm to optimize a bidirectional gated recurrently neural network, to predict air quality index time series and capture data dependence.