A novel ensemble reinforcement learning gated unit model for daily PM2.5 forecasting

Li, Yanfei; Liu, Zheyu; Liu, Hui

doi:10.1007/s11869-020-00948-x

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…In view of the strong long-term learning ability of LSTM (long-short-term memory), LSTME (consisting of two layers of LSTM and one fully linked layer) [21] is employed to forecast PM2.5 in Beijing and its MAPE (%) value is 14.94 % less than TDNN. The gate recurrent unit (GRU) model with a simpler structure can avoid the information redundancy caused by too many parameters, and achieves better prediction results than LSTM in the PM2.5 prediction of three cities [22]. Besides, combined with the integrated learning method, remarkable prediction results are achieved.…”

Section: Research Motivationmentioning

confidence: 99%

Transfer Learning-Driven Hourly PM2.5 Prediction Based on a Modified Hybrid Deep Learning

Yang,

Ismail,

et al. 2023

IEEE Access

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Haze is a major problem in China's air pollution, which not only hinders economic development, but also causes harm to people's health. PM2.5 (fine particulate matter) is the primary cause of haze. Therefore, the timely prevention and control of haze benefit the precise forecast of PM2.5 concentration. Air quality has high-dimensional, non-linear and complex characteristics. In this paper, a modified hybrid deep learning model is proposed under the framework of transfer learning, which can solve the problem of air quality prediction in the case of sparse data. The research focuses on solving the problem of inadequate feature extraction in existing studies, and and predicts PM2.5 concentration at multiple sites. In the domain of adaptive extraction of air pollutant characteristics, long and short-term neural networks and multi-layer perceptron are used to realize the long-term dependence and nonlinear transformation of features, respectively. The learned features can be shared by PM2.5 prediction tasks at multiple sites. The channel and spatial attention mechanisms are added to extract the key information in the representation target. In the whole network, the residual neural unit is used to increase the depth of the network and improve prediction accuracy. This paper discusses the experimental results in Beijing dataset from 2013 to 2017 and Hengshui dataset from 2020 to 2022. Based on the findings, it shows that compared with the classical deep learning models, hybrid deep learning models and the most recent transfer learning approaches, the network can obtain higher accuracy and better robustness, especially for the prediction of sites with sparse data. The RMSE value of TL-Modified compared with TL-LSTM and TL-CNN-LSTM models decreased by 38 %, 16.5% and 25.6 % at different sites, respectively.

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Section: Research Motivationmentioning

confidence: 99%

Transfer Learning-Driven Hourly PM2.5 Prediction Based on a Modified Hybrid Deep Learning

Yang,

Ismail,

et al. 2023

IEEE Access

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“…With more and more attention paid to air pollution, researchers have also proposed many spatiotemporal prediction models to predict air pollution. Li et al [15] presented a new ensemble reinforcement learning gated unit model. The key of this model is to establish a sub-series forecasting model by the SAE-GRU method.…”

Section: Pm25 Concentration Predictionmentioning

confidence: 99%

Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model

Zhang

Zhu

et al. 2022

Applied Sciences

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The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model.

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“…The attention mechanism gauges the impact of past states on future PM2.5 concentrations, refining predictive precision through an attention-based hierarchy. In another study, Li et al [13] introduced a gated cell model integrated with reinforcement learning, specifically the SAE-GRU method.…”

Section: Introductionmentioning

confidence: 99%

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Wu,

Zhu,

Wen

2024

PLoS ONE

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Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

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A novel ensemble reinforcement learning gated unit model for daily PM2.5 forecasting

Cited by 12 publications

References 37 publications

Transfer Learning-Driven Hourly PM2.5 Prediction Based on a Modified Hybrid Deep Learning

Transfer Learning-Driven Hourly PM2.5 Prediction Based on a Modified Hybrid Deep Learning

Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

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