Proceedings of IAHS
Proceedings of IAHS
PIAHS
Articles | Volume 379
Articles | Volume 379
https://doi.org/10.5194/piahs-379-335-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/piahs-379-335-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 379
Pre-conference publication
 | 
05 Jun 2018
Pre-conference publication |  | 05 Jun 2018

Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

Zhanjie Li, Jingshan Yu, Xinyi Xu, Wenchao Sun, Bo Pang, and Jiajia Yue

Viewed

Total article views: 4,429 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,537 763 129 4,429 143 179
  • HTML: 3,537
  • PDF: 763
  • XML: 129
  • Total: 4,429
  • BibTeX: 143
  • EndNote: 179
Views and downloads (calculated since 05 Jun 2018)
Cumulative views and downloads (calculated since 05 Jun 2018)

Viewed (geographical distribution)

Total article views: 3,324 (including HTML, PDF, and XML) Thereof 3,225 with geography defined and 99 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Feb 2026
Download
Short summary
Multi-model ensemble hydrological simulation has been an effective method for improving simulation accuracy. This study explored the feasibility of applying a multi-model ensemble simulation to the upper Yalongjiang River Basin. The results of the BPNN multi-model ensemble simulation are better than that of a single model. Multi-model ensemble simulation should become an important direction in hydrological simulation research.
Read more
Share
Special issue
Innovative water resources management – understanding...