Spatiotemporal integration of GCN and E-LSTM networks for PM2.5 forecasting

Kamali Mohammadzadeh, Ali; Salah, Halima; Jahanmahin, Roohollah; Hussain, Abd E Ali; Masoud, Sara; Huang, Yaoxian

doi:10.1016/j.mlwa.2023.100521

Cited by 2 publications

(1 citation statement)

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“…For example, considering that air quality monitoring data involve not only temporal but also spatial features, Ma et al constructed an LSTM-GCN model for predicting PM 2.5 concentration in the next hour by fusing Graph Convolutional Network (GCN) and Long Short-Term Memory Network (LSTM), which was applied in the Hunnan District of Shenyang and demonstrated higher accuracy than the traditional method [11]. Ali Kamali Mohammadzadeh et al proposed a spatio-temporal deep neural structure combining GCN and exogenous Long Short-Term Memory Network (E-LSTM) for predicting PM 2.5 air quality index (AQI), and the results of the study showed that this framework is significantly more accurate in predicting PM 2.5 AQI than the traditional LSTM and E-LSTM methods, and also shows good robustness to the network structure of EPA stations [12]. Li et al successfully constructed a hybrid model of a convolutional neural network (CNN) and LSTM, both of which performed well and demonstrated excellent performance in nonlinear time series forecasting [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of PM2.5 Concentration Based on Deep Learning, Multi-Objective Optimization, and Ensemble Forecast

Gao,

Mo,

2024

Sustainability

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Accurate and stable prediction of atmospheric PM2.5 concentrations is crucial for air pollution prevention and control. Existing studies usually rely on a single model or use a single evaluation criterion in multi-model ensemble weighted forecasts, neglecting the dual needs for accuracy and stability in PM2.5 forecast. In this study, a novel ensemble forecast model is proposed that overcomes these drawbacks by simultaneously taking into account both forecast accuracy and stability. Specifically, four advanced deep learning models—Long Short-Term Memory Network (LSTM), Graph Convolutional Network (GCN), Transformer, and Graph Sample and Aggregation Network (GraphSAGE)—are firstly introduced. And then, two combined models are constructed as predictors, namely LSTM–GCN and Transformer–GraphSAGE. Finally, a combined weighting strategy is adopted to assign weights to these two combined models using a multi-objective optimization algorithm (MOO), so as to carry out more accurate and stable predictions. The experiments are conducted on the dataset from 36 air quality monitoring stations in Beijing, and results show that the proposed model achieves more accurate and stable predictions than other benchmark models. It is hoped that this proposed ensemble forecast model will provide effective support for PM2.5 pollution forecast and early warning in the future.

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Section: Introductionmentioning

confidence: 99%