Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

Yan, Rui; Liao, Jiaqiang; Yang, Jie; Sun, Wei; Nong, Mingyue; Li, Feipeng

doi:10.1016/j.eswa.2020.114513

Cited by 262 publications

(106 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The model extracts the key factors of complex spatio-temporal relations to reduce error accumulation and propagation in the multi-step air quality forecasting. Rui Yan [24] established a multi-time, multi-site forecasting model based on spatiotemporal clustering for air quality forecasting. The spatiotemporal distribution characteristics were introduced into the forecasting processing.…”

Section: Prediction On Spatio-temporal Factorsmentioning

confidence: 99%

“…The prediction models based on deep learning [9][10][11][12][13][14] can extract the features existing in the air quality data and can achieve higher prediction accuracy. Some methods [15][16][17][18][19][20][21][22][23][24][25] simulate the temporal and spatial dependence of air quality data at the same time. But widely-used machine learning methods often suffer from high variability in performance in different circumstances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Air Quality Prediction Based on Integrated Dual LSTM Model

Chen

Guan

2021

IEEE Access

View full text Add to dashboard Cite

Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.

show abstract

Section: Prediction On Spatio-temporal Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction Based on Integrated Dual LSTM Model

Chen

Guan

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Existing studies show that machine learning-and deep learning-based models have been widely applied in atmospheric environmental modeling for the monitoring and prediction of air pollutants, such as the multilayer perceptron (MLP) model [26,27], the backpropagation neural network (BPNN) model [28], support vector regression (SVR) [28,29], the random forest (RF) model [30][31][32], the general regression neural network (GRNN) model [28,33], the recurrent neural network (RNN) model [34], and long short-term memory (LSTM)-based models [11,[35][36][37][38]. The careful reasoning process in machine learningbased models (such as MLP, SVR, and RF) is comparable to mathematical reasoning [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

et al. 2021

View full text Add to dashboard Cite

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

show abstract

“…Recently, a deep learning method has been widely used for air quality prediction and many research results have been presented [17,18]. For example, Kim et al [19] used a recurrent neural network (RNN) model to predict the concentration of air pollutants on subway platforms.…”

Section: Introductionmentioning

confidence: 99%

An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

Shi

Fang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

The air quality prediction is a very important and challenging task, especially PM2.5 (particles with diameter less than 2.5 μm) concentration prediction. To improve the accuracy of the PM2.5 concentration prediction, an improved integrated deep neural network method based on attention mechanism is proposed in this paper. Firstly, the influence of exogenous series of other sites on the central site is considered to determine the best relevant site. Secondly, the data of all relevant sites are input into the improved dual-stage two-phase (DSTP) model, then the PM2.5 prediction result of each site is obtained. Finally, with the PM2.5 prediction result of each site, the attention-based layer predicts the PM2.5 concentration at the central site. The experimental results show that the proposed model is superior to most of the latest models.

show abstract

Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

Cited by 262 publications

References 55 publications

Air Quality Prediction Based on Integrated Dual LSTM Model

Air Quality Prediction Based on Integrated Dual LSTM Model

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

Contact Info

Product

Resources

About