Abstract
Environment protection department need to grasp the concentration of PM2.5 in a future moment when monitoring. However, the existing PM2.5 prediction studies only forecast short-term time points, and cannot accurately give the trend of the next period of time. In this paper, a PM2.5 prediction model based on Att-ConvLSTM model integrated training method is established by the advantage of ConvLSTM to obtain spatiotemporal information. Then, Experiments were performed using DNN, ARIMA and LSTM as control model with Att-ConvLSTM model and used it for application test. The result demonstrated that prediction model can extract spatiotemporal features with attention mechanism and ConvLSTM. The model can reduce the generalization error of the model when predicting other observation points.
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Xu, Z., Lv, Y. (2020). Att-ConvLSTM: PM2.5 Prediction Model and Application. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_4
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DOI: https://doi.org/10.1007/978-3-030-32456-8_4
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