Time Series-Based PM 2.5 Concentration Prediction Model Incorporating Attention Mechanism

As a key indicator of air quality, effective forecasting of PM 2.5 concentration can provide key technical support for the scientific and precise implementation of air pollution prevention and control. However, predicting PM 2.5 concentrations faces challenges such as multiple influencing factors, long-term temporal dependencies, and inherent nonlinearity. Furthermore, traditional Long Short-Term Memory (LSTM) networks not only fail to effectively grasp the dependency relationships in long-time-span data, but also encounter difficulties in fully integrating and exploiting the information of numerous influencing factors. In order to solve these problems, a novel prediction model (OVMD–PeepholeLSTM–attention) for PM 2.5 concentration was presented in this study, which includes Peephole Long Short-Term Memory (PeepholeLSTM), optimal variational mode decomposition (OVMD) and an attention mechanism (AM). In this study, K modal components result from the initial decomposition of PM 2.5 monitoring data using OVMD. The obtained components are then individually predicted by the PeepholeLSTM–attention model, and the final prediction is reconstructed. The proposed model was comprehensively evaluated on PM 2.5 concentration monitoring data sets from Guangzhou and Shenzhen in China from 2020 to 2022, through a series of comparative experiments. The model proposed in this study is shown by experimental results to reduce mean absolute error (MAE) by approximately 39%, root mean square error (RMSE) by 45%, and increases the fitting coefficient ( R 2 ) by 0.0457 in Guangzhou compared to the single PeepholeLSTM model. The corresponding improvements in Shenzhen are 45% for MAE, 51% for RMSE, and 0.0765 for R 2 . This indicates that the model proposed in this paper exhibits higher accuracy in terms of predicting PM 2.5 concentrations, and the research results can provide a basis for quantitative assessment and scientific decision-making for the sustainable development of urban ecological environments.