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In recent years, air pollution caused by smog has caused extremely bad harm to human health and the environment, which must arouse people’s attention to air pollution. In order to predict PM2.5 concentration, the one-dimensional convolutional neural network and recurrent neural network (BIGRU) were combined to construct a combined model CBIGRU, and then the whale optimization algorithm was used to optimize the parameters of the network model to predict the PM2.5 concentration in Anqing City. Experimental results show that the optimized WOA CBiGRU neural network model has fast convergence speed, high prediction stability, and better accuracy than the traditional GRU and BIGRU models, and can be used to predict PM2.5 concentration.

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PM2.5 Concentration Prediction Model Based on WOA_CBiGRU

  • 会议论文

  • 发表日期：

    2025年

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  • Jiangping Yu,
  • Yuanbo Shi

摘要

In recent years, air pollution caused by smog has caused extremely bad harm to human health and the environment, which must arouse people’s attention to air pollution. In order to predict PM2.5 concentration, the one-dimensional convolutional neural network and recurrent neural network (BIGRU) were combined to construct a combined model CBIGRU, and then the whale optimization algorithm was used to optimize the parameters of the network model to predict the PM2.5 concentration in Anqing City. Experimental results show that the optimized WOA CBiGRU neural network model has fast convergence speed, high prediction stability, and better accuracy than the traditional GRU and BIGRU models, and can be used to predict PM2.5 concentration.