Daily PM10 concentration forecasting based on multiscale fusion support vector regression

Li, Yong; Yan, Tao

doi:10.3233/jifs-169555

Cited by 11 publications

(2 citation statements)

References 40 publications

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“…(2) MFSVR: this is a predictive model with machine learning technology based on SVR. To improve the prediction accuracy, the model uses a feature fusion method based on partial least squares to extract the original features and reduce the dimensions of the input variables of the SVR model [40].…”

Section: Compared Methodsmentioning

confidence: 99%

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Zou

Zhao

Duan

et al. 2021

Mobile Information Systems

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With the rapid development of the Internet of Things and Big Data, smart cities have received increasing attention. Predicting air quality accurately and efficiently is an important part of building a smart city. However, air quality prediction is very challenging because it is affected by many complex factors, such as dynamic spatial correlation between air quality detection sensors, dynamic temporal correlation, and external factors (such as road networks and points of interest). Therefore, this paper proposes a long short-term memory (LSTM) air quality prediction model based on a spatiotemporal attention mechanism (STA-LSTM). The model uses an encoder-decoder structure to model spatiotemporal features. A spatial attention mechanism is introduced in the encoder to capture the relative influence of surrounding sites on the prediction area. A temporal attention mechanism is introduced in the decoder to capture the time dependence of air quality. In addition, for spatial data such as point of interest (POI) and road networks, this paper uses the LINE graph embedding method to obtain a low-dimensional vector representation of spatial data to obtain abundant spatial features. This paper evaluates STA-LSTM on the Beijing dataset, and the root mean square error (RMSE) and R-squared ( R 2 ) indicators are used to compare with six benchmarks. The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.

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Section: Compared Methodsmentioning

confidence: 99%

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Zou

Zhao

Duan

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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“…Traditional methods such as linear regression [10,11], time series [12,13], gray model [14], support vector machine [15][16][17][18], etc. are often used to predict the concentration of pollutants.…”

Section: Introductionmentioning

confidence: 99%

Research on data correction method of micro air quality detector based on combination of partial least squares and random forest regression

Liu

Wang

et al. 2021

Preprint

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The issue of air quality has attracted more and more attention. The main methods for monitoring the concentration of pollutants in the air include national monitoring station monitoring and micro air quality detector testing. Since the electrochemical sensor of the micro air quality detector is susceptible to interference, the monitored data has a certain deviation. In this paper, the combined model of partial least square regression and random forest regression (PLS-RFR) is used to correct the detection data of the micro air quality detector. First, correlation analysis is used to find out the factors that affect the concentration of pollutants. Second, partial least squares regression is used to give the quantitative relationship of the influence of each influencing factor on the concentration of pollutants. Finally, the predicted value of partial least squares regression and various influencing factors are used as independent variables, and the pollutant concentration monitored by the national monitoring station is used as the dependent variable, and the PLS-RFR model is obtained with the help of random forest software package. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit (R2), and Root Mean Square Error (RMSE) are used as evaluation indicators to compare PLS-RFR model, support vector machine model and multilayer perceptron neural network. The results show that no matter which evaluation index, the prediction effect of the PLS-RFR model is the best, and the model has a good prediction effect in the training set or the test set, indicating that the model has good generalization ability. This model can play an active role in the promotion and deployment of micro air quality detectors.

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