Prediction of PM 2.5 Concentration Based on Deep Learning, Multi-Objective Optimization, and Ensemble Forecast

Zihang Gao, Xinyue Mo () and Huan Li ()
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Zihang Gao: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China
Xinyue Mo: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China
Huan Li: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China

Sustainability, 2024, vol. 16, issue 11, 1-15

Abstract: Accurate and stable prediction of atmospheric PM 2.5 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 PM 2.5 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 PM 2.5 pollution forecast and early warning in the future.

Keywords: air pollution; deep learning; multi-objective optimization; ensemble forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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