Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach

ZHAO Liang; LI Mengwei; ZHENG Yuqing; CUI Beibei; ZHU Xianchao

doi:10.11959/j.issn.2096-6652.202425

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Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach

Papers and Reports | 更新时间：2024-08-06

- Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 2, Pages: 253-261(2024)
- 作者机构：
  
  1.河南工业大学电气工程学院，河南郑州 450001
  2.河南工业大学人工智能与大数据学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61473114);The Science and Technology Research Project of Henan Provincial Science and Technology Department(242102211040);The Key Scientific and Technological Research Project of Henan Provincial Department of Education(24B520006;24A520014)
- DOI：10.11959/j.issn.2096-6652.202425
  CLC： TP399
- Received：07 April 2024，
  
  Revised：2024-05-23，
  
  Published：15 June 2024
- 稿件说明：
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赵亮,李梦威,郑玉卿等.二氧化氮浓度时空预测：一种区间二型直觉模糊神经网络方法[J].智能科学与技术学报,2024,06(02):253-261.

ZHAO Liang,LI Mengwei,ZHENG Yuqing,et al.Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):253-261.
赵亮,李梦威,郑玉卿等.二氧化氮浓度时空预测：一种区间二型直觉模糊神经网络方法[J].智能科学与技术学报,2024,06(02):253-261. DOI： 10.11959/j.issn.2096-6652.202425.

ZHAO Liang,LI Mengwei,ZHENG Yuqing,et al.Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):253-261. DOI： 10.11959/j.issn.2096-6652.202425.

摘要

空气中二氧化氮浓度的高低对环境保护和公共健康具有重要影响。目前二氧化氮浓度预测方法在表征时空关联性方面存在不足。鉴于此，提出了新的使用区间二型直觉模糊神经网络时空预测二氧化氮浓度的方法。首先，阐述了该区间二型直觉模糊神经网络框架，引入可变系数加权其隶属部分和非隶属部分的输出，并采用随机向量泛函链接神经网络作为规则后件；然后，为确定网络结构和参数，采用分层聚类算法得到模糊规则库，并通过最小二乘法优化网络后件的输出权值；最后，使用2018年1月至3月采集的北京市二氧化氮浓度真实数据进行数值验证。实验结果表明，与现有方法相比，该方法在短期和长期时空预测方面均取得了较高的预测精度和效率。

Abstract

The concentration of nitrogen dioxide (NO

) in the air significantly affects environmental protection and public health. Current methods for NO

concentration prediction lack sufficient characterization of spatiotemporal correlations. Therefore

this study proposes a novel approach using interval type-2 intuitionistic fuzzy neural networks (IT2IFNNs) for spatiotemporal prediction of NO

concentrations. Firstly

the framework of IT2IFNNs is elucidated

incorporating

variable coefficient weighting for its membership and non-membership outputs

and employing a random vector functional-link neural network (RVFLNN) as the rule consequent. Secondly

a hierarchical clustering algori

thm is employed to determine the fuzzy rule base and optimize the output weight values of the network consequents using least squares estimation. Finally

numerical validation is conducted using real NO

concentration data collected in Beijing from January to March 2018. Experimental results demonstrate that compared to existing methods

the proposed approach achieves superior prediction accuracy and efficiency in both short-term and long-term spatiotemporal prediction tasks.

关键词

Keywords

references

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