A Novel Hybrid Method to Predict PM2.5 Concentration Based on the SWT-QPSO-LSTM Hybrid Model

Abstract

PM2.5 concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM2.5 concentration, this paper proposes a hybrid prediction system based on the Synchrosqueezing Wavelet Transform (SWT) method, Quantum Particle Swarm Optimization (QPSO) algorithm, and Long Short-Term Memory (LSTM) model. First, the original data are denoised by the SWT method and taken as the input of the prediction model. Then, the main parameters of the LSTM model are optimized by global search based on the QPSO algorithm, which solves the problems of slow convergence and local extremum of traditional parameter training algorithms. Finally, the PM2.5 daily concentration data of Chengdu, Shijiazhuang, Shenyang, and Wuhan are predicted by the proposed SWT-QPSO-LSTM model, and the prediction results are compared with those of single prediction models and hybrid prediction models. The experimental results show that the proposed model achieves higher prediction precision and lower prediction error than other models.

A Vehicle Velocity Prediction Method with Kinematic Segment Recognition

“…In recent years, a LSTM neural network prediction model based on QPSO has been widely used in various disciplines. In terms of the PM2.5 concentration prediction, the prediction model of QPSO combined with LSTM can accurately predict the PM2.5 concentration after training [37]. In terms of the usage prediction of shared bicycles, the The forget gate determines what information to forget from the cell state, reads h t−1 and x t , and uses the sigmoid function for processing to determine the proportion of information forgotten at a moment in time on the cell state.…”

“…In recent years, a LSTM neural network prediction model based on QPSO has been widely used in various disciplines. In terms of the PM2.5 concentration prediction, the prediction model of QPSO combined with LSTM can accurately predict the PM2.5 concentration after training [37]. In terms of the usage prediction of shared bicycles, the prediction model of QPSO combined with LSTM could predict the number of bicycles needed per hour in the future day [38].…”

A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City

“…Applications of AI towards predicting PM concentrations can be found in the literature, with the first work of this kind published almost two decades ago [24]. Over last few years, the community has been actively exploring Deep Learning approaches to PM prediction, with very good results [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Despite the accurate predictions of ANNs, and the fact that they often outperform classical machine learning algorithms, they receive criticism for being "black boxes" [43].…”