Multi-site air quality prediction based on graph convolutional neural network-bi-directional LSTM model

LaLao Gao; MingChao Liao; DingJun Zhang

doi:10.1117/12.2667705

28 March 2023 Multi-site air quality prediction based on graph convolutional neural network-bi-directional LSTM model

LaLao Gao, MingChao Liao, DingJun Zhang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125664A (2023) https://doi.org/10.1117/12.2667705
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

To address the current problem of single-site prediction and inadequate extraction of spatial features for PM2.5 hourly concentration prediction, a graphical convolutional neural network (GCN) is proposed to obtain the spatial correlation between PM2.5 monitoring stations in Beijing by considering the features of time series in time and space, and assign weights according to the distance between stations to abstract into an undirected topological map. The missing data sequences are complemented by using a long and short-term memory network to extract temporal features on the time-series dataset, which are normalized and then fused with the components extracted by the GCN to make predictions. The experimental results show that GCN-BiLSTM has higher prediction accuracy and better results than single RNN, LSTM, and BiLSTM algorithms.

Citation Download Citation

LaLao Gao, MingChao Liao, and DingJun Zhang "Multi-site air quality prediction based on graph convolutional neural network-bi-directional LSTM model", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125664A (28 March 2023); https://doi.org/10.1117/12.2667705

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