Prediction of Pollutant Concentration Based on Spatial-Temporal Attention, ResNet and ConvLSTM.
Prediction of Pollutant Concentration Based on Spatial-Temporal Attention, ResNet and ConvLSTM.
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基于时空的注意，重新连接和探测的污染物浓度的预测。
DOI：
10.3390/s23218863
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发表时间：
2023-10-31
期刊：
影响因子：
--
通讯作者：
Li D
中科院分区：
文献类型：
--
作者：
Chen C;Qiu A;Chen H;Chen Y;Liu X;Li D
准确可靠的空气污染物浓度预测对于合理避免空气污染事件和政府的政策响应具有重要意义。但由于污染源、气象条件、转化过程的流动性和动态性，污染物浓度预测具有很大的不确定性和不稳定性，使得现有预测模型难以有效提取时空相关性。本文提出了一个强大的污染物预测模型（STA-ResConvLSTM），以实现准确的预测污染物浓度。该模型由基于残差神经网络（ResNet）的深度学习网络模型、时空注意力机制和卷积长短期记忆神经网络（ConvLSTM）组成。将时空注意机制嵌入到ResNet的每个残差单元中，形成一个新的具有时空注意机制的残差神经网络（STA-ResNet）。利用STA-ResNet对多个城市污染物浓度和气象数据的时空分布特征进行了深度提取。其输出用作ConvLSTM的输入，进一步分析以提取从STA-ResNet提取的初步时空分布特征。该模型实现了提取的特征序列的时空相关性，以准确预测未来的污染物浓度。此外，在龙京周边城市群的实验研究表明，预测模型的精度和稳定性优于各种流行的基线模型。对于单步预测任务，建议的污染物浓度预测模型表现良好，表现出的均方根误差（RMSE）为9.82。此外，即使对于1至48小时的污染物预测任务，我们进行了多步预测，并取得了令人满意的性能，能够达到13.49的平均RMSE值。
Accurate and reliable prediction of air pollutant concentrations is important for rational avoidance of air pollution events and government policy responses. However, due to the mobility and dynamics of pollution sources, meteorological conditions, and transformation processes, pollutant concentration predictions are characterized by great uncertainty and instability, making it difficult for existing prediction models to effectively extract spatial and temporal correlations. In this paper, a powerful pollutant prediction model (STA-ResConvLSTM) is proposed to achieve accurate prediction of pollutant concentrations. The model consists of a deep learning network model based on a residual neural network (ResNet), a spatial–temporal attention mechanism, and a convolutional long short-term memory neural network (ConvLSTM). The spatial–temporal attention mechanism is embedded in each residual unit of the ResNet to form a new residual neural network with the spatial–temporal attention mechanism (STA-ResNet). Deep extraction of spatial–temporal distribution features of pollutant concentrations and meteorological data from several cities is carried out using STA-ResNet. Its output is used as an input to the ConvLSTM, which is further analyzed to extract preliminary spatial–temporal distribution features extracted from the STA-ResNet. The model realizes the spatial–temporal correlation of the extracted feature sequences to accurately predict pollutant concentrations in the future. In addition, experimental studies on urban agglomerations around Long Beijing show that the prediction model outperforms various popular baseline models in terms of accuracy and stability. For the single-step prediction task, the proposed pollutant concentration prediction model performs well, exhibiting a root-mean-square error (RMSE) of 9.82. Furthermore, even for the pollutant prediction task of 1 to 48 h, we performed a multi-step prediction and achieved a satisfactory performance, being able to achieve an average RMSE value of 13.49.
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影响因子：
3.7
作者：
Huang F;Li X;Wang C;Xu Q;Wang W;Luo Y;Tao L;Gao Q;Guo J;Chen S;Cao K;Liu L;Gao N;Liu X;Yang K;Yan A;Guo X
通讯作者：
Guo X
影响因子：
4.3
作者：
Maleki H;Sorooshian A;Goudarzi G;Baboli Z;Birgani YT;Rahmati M
通讯作者：
Rahmati M
影响因子：
8.9
作者：
Li, Xiang;Peng, Ling;Chi, Tianhe
通讯作者：
Chi, Tianhe
影响因子：
7.7
作者：
Leong, W. C.;Kelani, R. O.;Ahmad, Z.
通讯作者：
Ahmad, Z.
影响因子：
3.3
作者：
Korkmaz, Deniz;Acikgoz, Hakan;Yildiz, Ceyhun
通讯作者：
Yildiz, Ceyhun