Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1394-1400.DOI: 10.11772/j.issn.1001-9081.2022030437
Special Issue: 第九届中国数据挖掘会议(CCDM 2022)
• China Conference on Data Mining 2022 (CCDM 2022) • Previous Articles Next Articles
Rui XU1, Shuang LIANG1, Hang WAN2(), Yimin WEN1, Shiming SHEN3, Jian LI1
Received:
2022-04-06
Revised:
2022-06-02
Accepted:
2022-06-15
Online:
2023-05-08
Published:
2023-05-10
Contact:
Hang WAN
About author:
LIANG Shuang, born in 1994, M. S. candidate. Her research interests include environmental forecasting, deep learning and environmental big data.Supported by:
许睿1, 梁爽1, 万航2(), 文益民1, 沈世铭3, 李建1
通讯作者:
万航
作者简介:
许睿(1977—),男,四川成都人,副教授,博士,CCF会员,主要研究方向:人工智能、深度学习与环境大数据、环境监测仪器仪表、环境遥感与地理信息系统基金资助:
CLC Number:
Rui XU, Shuang LIANG, Hang WAN, Yimin WEN, Shiming SHEN, Jian LI. Extraction of PM2.5 diffusion characteristics based on candlestick pattern matching[J]. Journal of Computer Applications, 2023, 43(5): 1394-1400.
许睿, 梁爽, 万航, 文益民, 沈世铭, 李建. 基于烛台图模式匹配的PM2.5扩散特征的提取[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1394-1400.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022030437
预测方法 | 资源使用 | 复杂度 | 预测精度 |
---|---|---|---|
基于物理模型的方法 | 高 | 高 | 低 |
基于机器学习的方法 | 高 | 高 | 一般 |
基于深度学习的方法 | 一般 | 一般 | 高 |
基于组合模型的方法 | 一般 | 一般 | 更高 |
Tab. 1 Comparison of characteristics of air pollutant concentration prediction methods
预测方法 | 资源使用 | 复杂度 | 预测精度 |
---|---|---|---|
基于物理模型的方法 | 高 | 高 | 低 |
基于机器学习的方法 | 高 | 高 | 一般 |
基于深度学习的方法 | 一般 | 一般 | 高 |
基于组合模型的方法 | 一般 | 一般 | 更高 |
匹配率 | 平均预测误差 | 匹配率 | 平均预测误差 |
---|---|---|---|
0.6 | 0.19 | 0.9 | 0.23 |
0.7 | 0.18 | 1.0 | 0.25 |
0.8 | 0.17 |
Tab. 2 Prediction error when matching rate changes
匹配率 | 平均预测误差 | 匹配率 | 平均预测误差 |
---|---|---|---|
0.6 | 0.19 | 0.9 | 0.23 |
0.7 | 0.18 | 1.0 | 0.25 |
0.8 | 0.17 |
方法 | 训练数据集 | 测试数据集 |
---|---|---|
基于AlexNet的方法 | 89.9 | 85.2 |
基于SVM的方法 | 91.3 | 91.4 |
基于VGG的方法 | 93.9 | 93.2 |
本文方法 | 97.8 | 95.1 |
Tab. 3 Accuracy comparison of different methods
方法 | 训练数据集 | 测试数据集 |
---|---|---|
基于AlexNet的方法 | 89.9 | 85.2 |
基于SVM的方法 | 91.3 | 91.4 |
基于VGG的方法 | 93.9 | 93.2 |
本文方法 | 97.8 | 95.1 |
方法 | PM2.5浓度上升 | PM2.5浓度下降 | PM2.5浓度未发生改变 | ||||||
---|---|---|---|---|---|---|---|---|---|
精确率 | 召回率 | F1分数 | 精确率 | 召回率 | F1分数 | 精确率 | 召回率 | F1分数 | |
基于AlexNet的方法 | 0.632 8 | 0.653 1 | 0.648 9 | 0.623 6 | 0.658 3 | 0.643 5 | 0.694 5 | 0.611 5 | 0.619 4 |
基于SVM的方法 | 0.670 9 | 0.608 1 | 0.638 2 | 0.691 2 | 0.604 4 | 0.627 2 | 0.708 4 | 0.600 7 | 0.604 8 |
基于VGG的方法 | 0.743 9 | 0.715 0 | 0.720 4 | 0.746 2 | 0.714 8 | 0.728 9 | 0.743 7 | 0.694 9 | 0.703 1 |
本文方法 | 0.800 7 | 0.843 2 | 0.812 7 | 0.820 7 | 0.853 9 | 0.829 5 | 0.710 8 | 0.623 2 | 0.637 5 |
Tab. 4 Comparison of different methods for predicting change of PM2.5 concentration
方法 | PM2.5浓度上升 | PM2.5浓度下降 | PM2.5浓度未发生改变 | ||||||
---|---|---|---|---|---|---|---|---|---|
精确率 | 召回率 | F1分数 | 精确率 | 召回率 | F1分数 | 精确率 | 召回率 | F1分数 | |
基于AlexNet的方法 | 0.632 8 | 0.653 1 | 0.648 9 | 0.623 6 | 0.658 3 | 0.643 5 | 0.694 5 | 0.611 5 | 0.619 4 |
基于SVM的方法 | 0.670 9 | 0.608 1 | 0.638 2 | 0.691 2 | 0.604 4 | 0.627 2 | 0.708 4 | 0.600 7 | 0.604 8 |
基于VGG的方法 | 0.743 9 | 0.715 0 | 0.720 4 | 0.746 2 | 0.714 8 | 0.728 9 | 0.743 7 | 0.694 9 | 0.703 1 |
本文方法 | 0.800 7 | 0.843 2 | 0.812 7 | 0.820 7 | 0.853 9 | 0.829 5 | 0.710 8 | 0.623 2 | 0.637 5 |
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