Air quality forecasting based on a sample convolution and interaction network model

QIN Yemei; HU Boju; FENG Yigui; ZHOU Fan; ZHAO Shen

doi:10.11959/j.issn.2096-6652.202428

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Air quality forecasting based on a sample convolution and interaction network model

Papers and Reports | 更新时间：2024-11-04

- Air quality forecasting based on a sample convolution and interaction network model
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 3, Pages: 356-366(2024)
- 作者机构：
  
  1.湖南工商大学智能工程与智能制造学院，湖南长沙 410205
  2.湘江实验室，湖南长沙 410205
  3.国防科技大学计算机学院，湖南长沙 410073
- 作者简介：
- 基金信息：
  
  The Major Project Fund of Key Scientific Research Project of Education Department of Hunan Province(21A0381;23A0464);The Key Projects of Xiangjiang Laboratory(22XJ01002;23XJ02006)
- DOI：10.11959/j.issn.2096-6652.202428
  CLC： TP183;X831
- Received：08 March 2024，
  
  Revised：2024-08-12，
  
  Published：15 September 2024
- 稿件说明：
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覃业梅,胡博飓,冯懿归等.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,2024,06(03):356-366.

QIN Yemei,HU Boju,FENG Yigui,et al.Air quality forecasting based on a sample convolution and interaction network model[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):356-366.
覃业梅,胡博飓,冯懿归等.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,2024,06(03):356-366. DOI： 10.11959/j.issn.2096-6652.202428.

QIN Yemei,HU Boju,FENG Yigui,et al.Air quality forecasting based on a sample convolution and interaction network model[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):356-366. DOI： 10.11959/j.issn.2096-6652.202428.

摘要

空气质量预测是治理和减轻空气污染的有效手段。为了提高预测精度，构建了一个新的空气质量预测模型，即样本卷积和交互网络（sample convolutional and interaction network，SCINet）模型。该模型由多个SCI-Block按照完全二叉树结构排列而成，通过翻转奇偶分裂重新排列生成一个新的序列，该结构能够更好地捕捉多变量大气污染物彼此间复杂的依赖关系和局部趋势。因为大气污染物监测数据具有季节性和随机性，所以使用两个SCINet进行叠加，既能扩大卷积运算的接受域，又能实现多分辨率分析。此外，通过模型深度及超参数调优，使其更加拟合空气质量时序数据特征，能够有助于提取目标变量的时间关系特征。最后，通过北京PM

2.5

数据集和北京多站点空气质量数据集进行实证研究，结果表明，SCINet模型具有更高的预测精度，在短期预测中其均方根误差比对比模型中表现最佳的DAQFF模型减少了31.59%，在长期预测中减少了24.36%。

Abstract

Air quality forecasting is an effective means of managing and mitigating air pollution. To enhance predicti

on accuracy

a new air quality prediction model

namely the sample convolutional and interaction network(SCINet)

is introduced 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 PM

2.5

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 (

RMSE

) 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

Keywords

references

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