基于集成深度学习模型的空气质量指数预测

大气污染严重危害居民的出行安全和身体健康,空气质量指数(AQI)是一种用于测量空气质量状况的综合指标,对AQI的预测可以提醒公众空气质量信息,使人们做出更明智的出行决策.通过提前预测空气质量的变化,政府和环保部门可以采取应急措施以减轻空气污染.本文提出基于卷积神经网络和门控循环单元的集成深度学习模型(CNN-GRU)对AQI进行预测.其中,卷积神经网络(CNN)提取污染气体浓度和AQI的时空特征并完成特征映射,门控循环单元(GRU)建模时序关系并高效完成计算.选取2014—2022年北京市和广州市的6种主要污染气体(PM_2.5、PM₁₀、SO₂、CO、NO₂、O₃)日平均质量浓度和AQI进行实例研究,使用CNN-GRU模型对AQI进行预测,与多元宇宙优化的广义回归神经网络模型(MVO-GRNN)、遗传算法优化的BP神经网络模型(GA-BP)对AQI的预测进行对比分析.实验结果表明,本文提出的CNN-GRU模型对AQI的预测误差最小.

Air quality index prediction based on integrated deep learning model

Air pollution seriously endangers the travel safety and health of residents.As a comprehensive indicator used to measure air quality condition,Air Quality Index (AQI) can alert the public to air quality and enable people to make more informed travel decisions.By predicting the change of air quality in advance,the government and environmental protection departments can take emergency measures to reduce air pollution.Here,we propose an integrated deep learning model based on Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) for AQI prediction.The CNN is used to extract the spatial and temporal characteristics of air pollutants and AQI and complete the feature mapping,while the GRU to model the temporal relationship and complete the calculation and AQI efficiently.The daily average concentrations of six major air pollutants (PM_2.5,PM₁₀,SO₂,CO,NO₂,O₃) in Beijing and Guangzhou during 2014-2022 are selected for example study,and the AQI is predicted using the CNN-GRU model.The results show that,compared with Multiverse-Optimized Generalized Regression Neural Network model (MVO-GRNN) and Genetic Algorithm-optimized BP neural network model (GA-BP),the proposed CNN-GRU model has the smallest prediction error for AQI.