Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 22, issue 12
Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-3859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-3859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 12
Research article
 | 
01 Dec 2022
Research article |  | 01 Dec 2022

Comparison of machine learning techniques for reservoir outflow forecasting

Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira

Viewed

Total article views: 2,064 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,405 610 49 2,064 29 30
  • HTML: 1,405
  • PDF: 610
  • XML: 49
  • Total: 2,064
  • BibTeX: 29
  • EndNote: 30
Views and downloads (calculated since 17 Jun 2022)
Cumulative views and downloads (calculated since 17 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,064 (including HTML, PDF, and XML) Thereof 1,988 with geography defined and 76 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2024
Download
Short summary
Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Read more
Special issue
Advances in machine learning for natural hazards risk...
Altmetrics
Final-revised paper
Preprint