Home Journals Articles Updates About
CN
image
收藏切换
Construction and Improvement of Clustering Model for Long-term Areal Rainfall Forecasting in the Upper Reaches of the Yangtze River
收藏切换
PDF
Li GU, Yu-bin CHEN, Chun-long LI
Water Resources and Power | 2023, 41(6) : 5 - 8
Less
收藏切换
Water Resources and Power | 2023, 41(6): 5-8
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Construction and Improvement of Clustering Model for Long-term Areal Rainfall Forecasting in the Upper Reaches of the Yangtze River
Full
Li GU, Yu-bin CHEN, Chun-long LI
Affiliations
  • Bureau of Hydrology, Changjiang Water Resources Commission of the Ministry of Water Resources, Wuhan 430010, China
Published: 2023-06-25 doi: 10.20040/j.cnki.1000-7709.2023.20230125
Outline
收藏切换

The cluster forecasting model is a long-term forecasting physical model based on mathematical statistics. This method has been applied in the long-term forecast of the upper reaches of the Yangtze River. After years of practice, there is space for improvement in accuracy and efficiency of the model. This paper selected the upper reaches of the Yangtze River as the demonstration area for forecast, the monthly areal rainfall from June to August as the forecast object, and used the coincidence rate of anomaly symbol and the percentage of anomaly error as the test methods of the model. And then the construction process and improvement of the model were described. Finally, the improved model was used to forecast the monthly area rainfall in the upper reaches of the Yangtze River from June to August during 2020 to 2022. The prediction results show that the improved model has a great improvement in the anomaly sign agreement rate and the percentage of anomaly error, and it has been implemented easily, which has certain reference value.

forecast  /  cluster  /  model  /  the upper reaches of the Yangtze River  /  areal rainfall
Li GU, Yu-bin CHEN, Chun-long LI. Construction and Improvement of Clustering Model for Long-term Areal Rainfall Forecasting in the Upper Reaches of the Yangtze River[J]. Water Resources and Power, 2023 , 41 (6) : 5 -8 . DOI: 10.20040/j.cnki.1000-7709.2023.20230125
Appendix
Less
Year 2023 volume 41 Issue 6
PDF
96
12
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230125
  • Receive Date：2023-02-03
  • Online Date：2026-01-28
  • Published：2023-06-25
Article Data
Affiliations
History
  • Received：2023-02-03
  • Revised：2023-03-21
Funding
Affiliations
    Bureau of Hydrology, Changjiang Water Resources Commission of the Ministry of Water Resources, Wuhan 430010, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230125
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House