A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)

Abstract

PM2.5 is one of the most important pollutants related to air quality, and the increase of its concentration will aggravate the threat to people's health. Therefore, the prediction of surface PM2.5 concentration is of great significance to human health protection. In this study, A hybrid CNN-LSTM model is developed by combining the convolutional neural network (CNN) with the long short-term memory (LSTM) neural network for forecasting the next 24h PM2.5 concentration in Beijing, which makes full use of their advantages that CNN can effectively extract the features related to air quality and the LSTM can reflect the long term historical process of input time series data. The air quality data of the last 7days and the PM2.5 concentration of the next day are first set as the input and output of the model due to the periodicity, respectively. Subsequently four models namely univariate LSTM model, multivariate LSTM model, univariate CNN-LSTM model and multivariate CNN-LSTM model, are established for PM2.5 concentration prediction. Finally, mean absolute error (MAE) and root mean square error (RMSE) are employed to evaluate the performance of these models and results show that the proposed multivariate CNN-LSTM model performs the best results due to low error and short training time.

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

“…The combination of CNN and LSTM models have also been actively explored [18,[34][35][36]. CNN-LSTM may improve the accuracy for PM 2.5 prediction, as reported by Li et al [37], where the authors implemented a 1D CNN to extract features from sequence data and used LSTM to predict future values. In many real problems, input data may come from many resources, constructing spatiotemporal dependencies as explained by Qi et al [34].…”

Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

“…As a classic deep learning model, the recurrent neural network is widely used in the prediction research of nonlinear systems. For example, Song et al solved the prediction of air quality problems by combining LSTM and Kalman [12], and Li et al applied LSTM to the prediction of PM2.5 concentration [13]. BiLSTM neural network [14] is a variant of the LSTM neural network [15].…”

Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

“…LSTM's key merits are that it can resolve to vanish gradient issues and explore sequential data relationships through unique (input, forget, output) gates. The amalgamation of CNN with LSTM is fast becoming a popular area of research in AQ and there are some important global studies such as [17][18][19]. However, the construction of the hourly APF system for TSP and especially for Australia is yet to be explored.…”