Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2643-2650.DOI: 10.11772/j.issn.1001-9081.2023081169
• Frontier and comprehensive applications • Previous Articles
Qianhong SHI1, Yan YANG1(), Yongquan JIANG1, Xiaocao OUYANG1, Wubo FAN2, Qiang CHEN2, Tao JIANG2, Yuan LI2
Received:
2023-08-31
Revised:
2023-09-11
Accepted:
2023-10-09
Online:
2024-08-22
Published:
2024-08-10
Contact:
Yan YANG
About author:
SHI Qianhong, born in 1999, M. S. candidate. His research interests include data mining, sequential data analysis.Supported by:
石乾宏1, 杨燕1(), 江永全1, 欧阳小草1, 范武波2, 陈强2, 姜涛2, 李媛2
通讯作者:
杨燕
作者简介:
石乾宏(1999—),男,河南荥阳人,硕士研究生,主要研究方向:数据挖掘、序列数据分析基金资助:
CLC Number:
Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction[J]. Journal of Computer Applications, 2024, 44(8): 2643-2650.
石乾宏, 杨燕, 江永全, 欧阳小草, 范武波, 陈强, 姜涛, 李媛. 面向空气质量预测的多粒度突变拟合网络[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2643-2650.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023081169
数据集 | 监测站点数 | PM2.5浓度平均值 | PM2.5浓度标准差 |
---|---|---|---|
北京 | 35 | 84.20 | 82.53 |
天津 | 24 | 80.21 | 66.32 |
深圳 | 11 | 33.02 | 22.18 |
Tab. 1 Details of experiment datasets
数据集 | 监测站点数 | PM2.5浓度平均值 | PM2.5浓度标准差 |
---|---|---|---|
北京 | 35 | 84.20 | 82.53 |
天津 | 24 | 80.21 | 66.32 |
深圳 | 11 | 33.02 | 22.18 |
模型 | 网络平均 训练时间 | 模型 | 网络平均 训练时间 |
---|---|---|---|
LSTM | 6 | PM2.5-GNN | 76 |
NSTransformers | 43 | Deep-AIR | 61 |
DCRNN | 63 | MACFN | 92 |
MSSTN | 87 |
Tab. 2 Comparison of single round training time among related models
模型 | 网络平均 训练时间 | 模型 | 网络平均 训练时间 |
---|---|---|---|
LSTM | 6 | PM2.5-GNN | 76 |
NSTransformers | 43 | Deep-AIR | 61 |
DCRNN | 63 | MACFN | 92 |
MSSTN | 87 |
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 35.21 | 57.39 | 0.39 | 39.12 | 61.71 | 0.31 | 45.16 | 67.68 | 0.17 | 52.19 | 75.69 | 0.01 |
NSTransformers | 23.58 | 41.53 | 0.69 | 32.80 | 54.36 | 0.47 | 44.87 | 65.90 | 0.23 | 50.46 | 71.96 | 0.09 |
DCRNN | 26.67 | 47.92 | 0.57 | 34.88 | 56.74 | 0.42 | 44.76 | 66.37 | 0.21 | 52.02 | 75.35 | 0.01 |
MSSTN | 23.01 | 41.33 | 0.69 | 33.80 | 53.83 | 0.47 | 43.66 | 64.93 | 0.25 | 50.40 | 72.78 | 0.05 |
PM2.5-GNN | 24.73 | 43.26 | 0.67 | 34.39 | 55.09 | 0.44 | 43.44 | 65.73 | 0.23 | 51.78 | 74.92 | 0.02 |
Deep-AIR | 22.52 | 40.80 | 0.71 | 32.89 | 53.61 | 0.47 | 43.83 | 65.42 | 0.25 | 50.30 | 72.52 | 0.06 |
MACFN | 21.01 | 35.81 | 0.78 | 30.38 | 46.44 | 0.58 | 39.91 | 57.35 | 0.39 | 46.86 | 67.44 | 0.13 |
Tab. 3 Performance comparison of MACFN and baseline models on Beijing dataset
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 35.21 | 57.39 | 0.39 | 39.12 | 61.71 | 0.31 | 45.16 | 67.68 | 0.17 | 52.19 | 75.69 | 0.01 |
NSTransformers | 23.58 | 41.53 | 0.69 | 32.80 | 54.36 | 0.47 | 44.87 | 65.90 | 0.23 | 50.46 | 71.96 | 0.09 |
DCRNN | 26.67 | 47.92 | 0.57 | 34.88 | 56.74 | 0.42 | 44.76 | 66.37 | 0.21 | 52.02 | 75.35 | 0.01 |
MSSTN | 23.01 | 41.33 | 0.69 | 33.80 | 53.83 | 0.47 | 43.66 | 64.93 | 0.25 | 50.40 | 72.78 | 0.05 |
PM2.5-GNN | 24.73 | 43.26 | 0.67 | 34.39 | 55.09 | 0.44 | 43.44 | 65.73 | 0.23 | 51.78 | 74.92 | 0.02 |
Deep-AIR | 22.52 | 40.80 | 0.71 | 32.89 | 53.61 | 0.47 | 43.83 | 65.42 | 0.25 | 50.30 | 72.52 | 0.06 |
MACFN | 21.01 | 35.81 | 0.78 | 30.38 | 46.44 | 0.58 | 39.91 | 57.35 | 0.39 | 46.86 | 67.44 | 0.13 |
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 27.56 | 38.89 | 0.19 | 29.93 | 41.50 | 0.14 | 33.77 | 43.72 | 0.06 | 34.86 | 44.72 | 0.01 |
NSTransformers | 17.39 | 27.03 | 0.62 | 23.30 | 34.15 | 0.40 | 29.55 | 40.88 | 0.15 | 33.51 | 44.85 | 0.01 |
DCRNN | 17.96 | 27.51 | 0.60 | 24.01 | 35.23 | 0.34 | 31.02 | 42.80 | 0.08 | 34.55 | 44.75 | 0.01 |
MSSTN | 16.76 | 26.28 | 0.63 | 23.07 | 33.90 | 0.41 | 30.15 | 40.57 | 0.16 | 33.97 | 43.96 | 0.01 |
PM2.5-GNN | 17.63 | 27.39 | 0.61 | 23.45 | 34.36 | 0.39 | 30.37 | 41.38 | 0.12 | 34.25 | 45.83 | 0.01 |
Deep-AIR | 17.39 | 26.94 | 0.61 | 23.54 | 34.85 | 0.38 | 29.72 | 41.19 | 0.13 | 33.96 | 44.29 | 0.01 |
MACFN | 16.63 | 24.32 | 0.68 | 22.31 | 30.56 | 0.47 | 28.17 | 37.94 | 0.29 | 33.49 | 43.41 | 0.03 |
Tab. 4 Performance comparison of MACFN and baseline models on Tianjin dataset
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 27.56 | 38.89 | 0.19 | 29.93 | 41.50 | 0.14 | 33.77 | 43.72 | 0.06 | 34.86 | 44.72 | 0.01 |
NSTransformers | 17.39 | 27.03 | 0.62 | 23.30 | 34.15 | 0.40 | 29.55 | 40.88 | 0.15 | 33.51 | 44.85 | 0.01 |
DCRNN | 17.96 | 27.51 | 0.60 | 24.01 | 35.23 | 0.34 | 31.02 | 42.80 | 0.08 | 34.55 | 44.75 | 0.01 |
MSSTN | 16.76 | 26.28 | 0.63 | 23.07 | 33.90 | 0.41 | 30.15 | 40.57 | 0.16 | 33.97 | 43.96 | 0.01 |
PM2.5-GNN | 17.63 | 27.39 | 0.61 | 23.45 | 34.36 | 0.39 | 30.37 | 41.38 | 0.12 | 34.25 | 45.83 | 0.01 |
Deep-AIR | 17.39 | 26.94 | 0.61 | 23.54 | 34.85 | 0.38 | 29.72 | 41.19 | 0.13 | 33.96 | 44.29 | 0.01 |
MACFN | 16.63 | 24.32 | 0.68 | 22.31 | 30.56 | 0.47 | 28.17 | 37.94 | 0.29 | 33.49 | 43.41 | 0.03 |
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 8.98 | 12.35 | 0.21 | 9.89 | 13.40 | 0.11 | 10.72 | 14.26 | 0.02 | 12.29 | 15.82 | 0.02 |
NSTransformers | 5.72 | 8.74 | 0.64 | 7.84 | 11.23 | 0.38 | 9.59 | 13.25 | 0.14 | 10.24 | 13.74 | 0.06 |
DCRNN | 6.01 | 9.16 | 0.59 | 8.48 | 12.14 | 0.28 | 10.22 | 14.01 | 0.05 | 11.29 | 14.90 | 0.01 |
MSSTN | 5.90 | 8.75 | 0.64 | 8.13 | 11.45 | 0.35 | 9.88 | 13.54 | 0.10 | 10.71 | 14.15 | 0.01 |
PM2.5-GNN | 5.89 | 8.96 | 0.61 | 8.28 | 11.94 | 0.30 | 9.85 | 13.53 | 0.11 | 11.00 | 14.65 | 0.01 |
Deep-AIR | 5.85 | 8.77 | 0.63 | 8.23 | 11.67 | 0.33 | 9.98 | 13.59 | 0.10 | 10.90 | 14.50 | 0.01 |
MACFN | 5.83 | 8.73 | 0.63 | 7.94 | 11.19 | 0.39 | 9.42 | 13.13 | 0.16 | 10.19 | 13.71 | 0.06 |
Tab. 5 Performance comparison of MACFN and baseline models on Shenzhen dataset
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
LSTM | 8.98 | 12.35 | 0.21 | 9.89 | 13.40 | 0.11 | 10.72 | 14.26 | 0.02 | 12.29 | 15.82 | 0.02 |
NSTransformers | 5.72 | 8.74 | 0.64 | 7.84 | 11.23 | 0.38 | 9.59 | 13.25 | 0.14 | 10.24 | 13.74 | 0.06 |
DCRNN | 6.01 | 9.16 | 0.59 | 8.48 | 12.14 | 0.28 | 10.22 | 14.01 | 0.05 | 11.29 | 14.90 | 0.01 |
MSSTN | 5.90 | 8.75 | 0.64 | 8.13 | 11.45 | 0.35 | 9.88 | 13.54 | 0.10 | 10.71 | 14.15 | 0.01 |
PM2.5-GNN | 5.89 | 8.96 | 0.61 | 8.28 | 11.94 | 0.30 | 9.85 | 13.53 | 0.11 | 11.00 | 14.65 | 0.01 |
Deep-AIR | 5.85 | 8.77 | 0.63 | 8.23 | 11.67 | 0.33 | 9.98 | 13.59 | 0.10 | 10.90 | 14.50 | 0.01 |
MACFN | 5.83 | 8.73 | 0.63 | 7.94 | 11.19 | 0.39 | 9.42 | 13.13 | 0.16 | 10.19 | 13.71 | 0.06 |
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
MACFN-ng | 22.66 | 37.04 | 0.72 | 31.60 | 48.06 | 0.53 | 40.72 | 58.69 | 0.31 | 47.74 | 68.07 | 0.08 |
MACFN-nt | 23.34 | 38.55 | 0.71 | 31.14 | 49.04 | 0.53 | 40.32 | 59.23 | 0.33 | 48.68 | 68.82 | 0.09 |
MACFN-ns | 22.94 | 40.80 | 0.67 | 33.45 | 53.06 | 0.47 | 43.57 | 64.80 | 0.28 | 49.95 | 73.76 | 0.04 |
MACFN-no | 21.74 | 36.65 | 0.73 | 30.89 | 47.37 | 0.55 | 40.94 | 59.66 | 0.30 | 50.19 | 70.53 | 0.09 |
MACFN-nm1 | 21.94 | 36.73 | 0.73 | 31.07 | 46.52 | 0.56 | 40.62 | 58.82 | 0.32 | 48.77 | 67.17 | 0.11 |
MACFN-nm2 | 21.73 | 36.53 | 0.73 | 31.03 | 46.69 | 0.56 | 40.45 | 58.23 | 0.33 | 49.54 | 68.33 | 0.08 |
MACFN-nm3 | 21.86 | 36.48 | 0.73 | 31.62 | 46.74 | 0.54 | 40.68 | 57.87 | 0.35 | 48.61 | 68.95 | 0.08 |
MACFN | 21.01 | 35.81 | 0.78 | 30.38 | 46.44 | 0.58 | 39.91 | 57.35 | 0.39 | 46.86 | 67.44 | 0.13 |
Tab. 6 Performance comparison of variant models with MACFN
模型 | 3 h | 6 h | 12 h | 24 h | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | MAE | RMSE | R² | |
MACFN-ng | 22.66 | 37.04 | 0.72 | 31.60 | 48.06 | 0.53 | 40.72 | 58.69 | 0.31 | 47.74 | 68.07 | 0.08 |
MACFN-nt | 23.34 | 38.55 | 0.71 | 31.14 | 49.04 | 0.53 | 40.32 | 59.23 | 0.33 | 48.68 | 68.82 | 0.09 |
MACFN-ns | 22.94 | 40.80 | 0.67 | 33.45 | 53.06 | 0.47 | 43.57 | 64.80 | 0.28 | 49.95 | 73.76 | 0.04 |
MACFN-no | 21.74 | 36.65 | 0.73 | 30.89 | 47.37 | 0.55 | 40.94 | 59.66 | 0.30 | 50.19 | 70.53 | 0.09 |
MACFN-nm1 | 21.94 | 36.73 | 0.73 | 31.07 | 46.52 | 0.56 | 40.62 | 58.82 | 0.32 | 48.77 | 67.17 | 0.11 |
MACFN-nm2 | 21.73 | 36.53 | 0.73 | 31.03 | 46.69 | 0.56 | 40.45 | 58.23 | 0.33 | 49.54 | 68.33 | 0.08 |
MACFN-nm3 | 21.86 | 36.48 | 0.73 | 31.62 | 46.74 | 0.54 | 40.68 | 57.87 | 0.35 | 48.61 | 68.95 | 0.08 |
MACFN | 21.01 | 35.81 | 0.78 | 30.38 | 46.44 | 0.58 | 39.91 | 57.35 | 0.39 | 46.86 | 67.44 | 0.13 |
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