智能科学与技术学报2024，Vol.6Issue(3)：356-366,11.DOI:10.11959/j.issn.2096-6652.202428
基于一种样本卷积和交互网络模型的空气质量预测
Air quality forecasting based on a sample convolution and interaction network model
摘要
Abstract
Air quality forecasting is an effective means of managing and mitigating air pollution.To enhance prediction accuracy,a new air quality prediction model,namely the sample convolutional and interaction network(SCINet),is intro-duced in this paper.The model is composed of multiple SCI-Blocks arranged in a complete binary tree structure.Whereafter,the time series is rearranged through flipping odd-even splits,and a new sequence is generated,which is able to capture the complex dependencies and local trends of multivariate atmospheric pollutants better.Given the seasonality and randomness of monitoring data for atmospheric pollutant,the paper employs two SCINets for stacking,which not only expands the receptive field of convolutional operations,but also enables multi-resolution analysis.Furthermore,through the optimization of model depth and hyperparameters,the model may fit the temporal characteristics of air quality time series data better,which is helpful to extract the temporal relationship features of the target variable.In the end,the Beijing PM2.5 dataset and the Beijing multi-site air quality dataset are utilized to evaluate the effectiveness of SCINet.The results show that SCINet has higher prediction accuracy,whose the root mean square error(δRMSE)is reduced by 31.59%in short-term prediction and 24.36%in long-term prediction compared with the best-performing DAQFF model.关键词
空气质量预测/SCINet/卷积神经网络/时间序列Key words
air quality forecasting/SCINet/CNN/time series分类
信息技术与安全科学引用本文复制引用
覃业梅,胡博飓,冯懿归,周帆,赵慎..基于一种样本卷积和交互网络模型的空气质量预测[J].智能科学与技术学报,2024,6(3):356-366,11.基金项目
湖南省教育厅科学研究重点项目(No.21A0381,No.23A0464) （No.21A0381,No.23A0464）
湘江实验室重大项目(No.22XJ01002,No.23XJ02006) The Major Project Fund of Key Scientific Research Project of Education Department of Hunan Province(No.21A0381,No.23A0464),The Key Projects of Xiangjiang Laboratory(No.22XJ01002,No.23XJ02006) （No.22XJ01002,No.23XJ02006）
