基于CNN-GRU-SSA组合模型的PM2.5浓度预测
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桂林理工大学

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P228

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国家自然科学(41864002)


Prediction of PM2.5 concentration based on CNN-GRU-SSA combined model
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Guilin University of Technology

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    摘要:

    为了解决门控循环单元(Gated Recurrent Unit,GRU)超参数选取困难的问题,提出一种基于卷积神经网络(Convolutional Neural Networks,CNN)、门控循环单元和麻雀搜索算法(Sparrow Search Algorithm,SSA)的组合模型(CNN-GRU-SSA)。首先利用CNN对输入的多维数据集进行特征提取;然后将CNN提取到的特征输入GRU模型;最后使用SSA算法优化GRU模型的超参数,并将其应用于PM2.5浓度预测。本文选取西部城市成都与东部城市杭州作为研究区域,使用2021年12月1日~2022年2月13日的大气污染物、气象因素、边界层高度(Boundary Layer Height,BLH)以及大气水汽(Precipitable Water Vapor,PWV)的小时数据进行建模,分别预测两市2022年2月14~2月28日PM2.5浓度变化。实验结果表明,CNN-GRU-SSA模型预测精度与其它模型相比有明显提高,其中成都的预测值最贴近实际。

    Abstract:

    A combined model called CNN-GRU-SSA is proposed to address the challenging issue of hyperparameter selection for Gated Recurrent Unit (GRU). This model integrates Convolutional Neural Networks (CNN), Gated Recurrent Units, and the Sparrow Search Algorithm (SSA). The proposed approach begins by employing CNN to extract features from the multidimensional dataset inputs. Subsequently, the features extracted by CNN are fed into the GRU model. Lastly, the SSA algorithm is utilized to optimize the hyperparameters of the GRU model, which is then applied to predict PM2.5 concentrations. This study focuses on the western city of Chengdu and the eastern city of Hangzhou as the research areas. Hourly data from December 1, 2021, to February 13, 2022, including atmospheric pollutants, meteorological factors, Boundary Layer Height (BLH), and Precipitable Water Vapor (PWV), were utilized for modeling. The aim was to predict the changes in PM2.5 concentrations from February 14 to February 28, 2022, for both cities. The experimental results indicate that the CNN-GRU-SSA model exhibits a significantly improved predictive accuracy compared to other models. Among these, the predicted values for Chengdu closely match the actual observations.

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林买金,张露露,唐友兵,等. 基于CNN-GRU-SSA组合模型的PM2.5浓度预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-08-17
  • 最后修改日期:2023-12-13
  • 录用日期:2023-12-29
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