PM2.5 volatility prediction by XGBoost-MLP based on GARCH models

Dai, Hongbin; Huang, Guangqiu; Zeng, Huibin; Zhou, Fangyu

doi:10.1016/j.jclepro.2022.131898

Cited by 65 publications

(28 citation statements)

References 45 publications

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“…This paper selects the daily average historical meteorological factor data (CO, NO 2 , SO 2 , PM10, O 3 , wind speed, average air pressure, wind direction, average temperature, and relative humidity) of Jinan City in 2019 as input data. In previous studies, these 10 meteorological factors are more important in influencing PM2.5 (Dai et al, 2022). The first 330 days are used as training data, and the last 35 days are used as test data.…”

Section: Methodsmentioning

confidence: 90%

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

Zhang

Yan

2023

Meteorological Applications

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With the increasingly severe problem of PM2.5 environmental pollution, the threat to human health is gradually increasing. Therefore, accurate prediction of PM2.5 concentration is of great significance to the healthy life of human beings. To make up for the deficiencies of previous studies on PM2.5 concentration prediction, a spatiotemporal model (Spatiotemporal prediction model of label distribution, LDSPM) for PM2.5 concentration prediction based on the K-Core algorithm concept and label distribution learning was proposed. Leveraging K-Core ideas and the label distribution support vector regression model of the label distribution paradigm, the influence weight of each meteorological factor on PM2.5 concentration in each piece of data was obtained with the decomposition of meteorological factors using the complete ensemble empirical mode decomposition of adaptive noise. Using a long short-term memory neural network to predict each decomposed signal and obtain the forecast data of meteorological factors. Finally, according to the expected weight and meteorological factor data, a particle swarm optimization extreme learning machine is used to train the prediction, and the predicted value of PM2.5 is obtained. The experimental results show that the forecasting model performs better than other combined and single forecasting models. It provides new directions and ideas for PM2.5 concentration prediction.

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Section: Methodsmentioning

confidence: 90%

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

Zhang

Yan

2023

Meteorological Applications

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“…Machine learning has been a popular choice for air quality forecasting because it is good at dealing with nonlinear problems. Dai et al 21 set up a hybrid model by using a multilayer perception that could predict the concentration and fluctuation in different regions more effectively. Ketu et al 22 combined the adjustment of kernel scales with a support vector machine 23 , which allows for an accurate classification of air quality.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

Zhao

Cai

et al. 2023

Sci Rep

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China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

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“…Furthermore, the modified WOA was used to optimize the hyperparameters of the model. Dai Hongbin et al 17 proposed an XGBoost-GARCH-MLP mixed model combining XGBoost, GARCH models and MLP models to predict PM2.5 concentration and volatility. The proposed model has good performance in the long-term forecasting process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of air quality index based on the SSA-BiLSTM-LightGBM model

Zhang

Jiang

2023

Sci Rep

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The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995.

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PM2.5 volatility prediction by XGBoost-MLP based on GARCH models

Cited by 65 publications

References 45 publications

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

Prediction of air quality index based on the SSA-BiLSTM-LightGBM model

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