基于多阶段聚类的PM<sub>2.5</sub>质量浓度预测及对比研究

金宇凯; 李志生; 欧耀春; 张华刚; 曾江毅; 陈搏超

doi:10.12052/gdutxb.210157

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基于多阶段聚类的PM_2.5质量浓度预测及对比研究

更新时间：2026-05-27

- 基于多阶段聚类的PM_2.5质量浓度预测及对比研究
- Prediction and Comparative Study of PM_2.5 Concentration Based on Multi-stage Clustering
- 广东工业大学学报 2023年40卷第3期页码：17-24
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 作者简介：
  
  金宇凯(1998–)，男，硕士研究生，主要研究方向为机器学习和室内空气品质
  李志生(1972–)，男，副教授，博士，主要研究方向为建筑环境与室内污染防治，E-mail：chinagzlzs@126.com
- 基金信息：
  
  广东省自然科学基金资助项目(S2011040003755);广东省自然科学基金资助项目(2016A030313711)
- DOI：10.12052/gdutxb.210157
  中图分类号： X513
- 网络首发：2023-05-25，
  
  纸质出版：2023
- 稿件说明：
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金宇凯, 李志生, 欧耀春, 张华刚, 曾江毅, 陈搏超. 基于多阶段聚类的PM_2.5质量浓度预测及对比研究[J]. 广东工业大学学报, 2023, 40(3): 17-24. DOI: 10.12052/gdutxb.210157.

Jin Yu-kai, Li Zhi-sheng, Ou Yao-chun, et al. Prediction and Comparative Study of PM_2.5 Concentration Based on Multi-stage Clustering[J]. 2023, 40(3): 17-24. DOI: 10.12052/gdutxb.210157.
金宇凯, 李志生, 欧耀春, 张华刚, 曾江毅, 陈搏超. 基于多阶段聚类的PM_2.5质量浓度预测及对比研究[J]. 广东工业大学学报, 2023, 40(3): 17-24. DOI: 10.12052/gdutxb.210157. DOI：

Jin Yu-kai, Li Zhi-sheng, Ou Yao-chun, et al. Prediction and Comparative Study of PM_2.5 Concentration Based on Multi-stage Clustering[J]. 2023, 40(3): 17-24. DOI: 10.12052/gdutxb.210157. DOI：

摘要

本文提出了一个基于多阶段聚类的深度神经网络(Deep Neural Network，DNN)预测模型，用于多步骤PM

2.5

质量浓度预测。建议的模型包括分解聚类和预测。在聚类部分中，第1阶段采用的是HDBSCAN(Hierarchical Density-based Spatial Clustering of Applications with Noise，HDB)密度聚类来剔除噪点，在此基础上，再进行第2阶段聚类。第2阶段聚类采用的是Kmeans、Agglomerative、高斯混合以及BIRCH聚类算法(Balanced Iterative Reducing and Clustering Using Hierarchies)4种聚类算法。在预测部分中，使用了DNN作为预测器，选取了深圳市11个空气质量监测站的2015全年逐时数据来验证模型的有效性。实验结果表明，基于多阶段聚类的预测模型适合PM

2.5

质量浓度的多步高精度预测，性能优于无聚类预测模型以及单阶段聚类预测模型。

Abstract

A deep neural network (DNN) prediction model based on multi-stage clustering is proposed for multi-step PM

2.5

concentration prediction. The proposed model includes decomposition

clustering and prediction. In the part of clustering

the first stage uses HDBscan density clustering to eliminate the noise

and then carries on the second stage clustering. In the second stage

Kmeans

AHClomerative

Gaussian mixture and birch clustering algorithms are used. In the prediction part

the deep neural network (DNN) is used as the predictor

and the hourly data of 11 air quality monitoring stations in Shenzhen are selected

to verify the effectiveness of the model. The experimental results show that the prediction model based on multi-stage clustering is suitable for multi-step high-precision prediction of PM concentration

and its performance is better than DNN model and single-stage clustering prediction model.

关键词

Keywords

references

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基于多阶段聚类的PM2.5质量浓度预测及对比研究

Prediction and Comparative Study of PM2.5 Concentration Based on Multi-stage Clustering

DOI：10.12052/gdutxb.210157

摘要

Abstract

关键词

Keywords

references

基于多阶段聚类的PM_2.5质量浓度预测及对比研究

Prediction and Comparative Study of PM_2.5 Concentration Based on Multi-stage Clustering