Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 24
Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4529-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-4529-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 24
Research article
 | 
20 Dec 2023
Research article |  | 20 Dec 2023

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang

Viewed

Total article views: 2,276 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,670 539 67 2,276 102 47 54
  • HTML: 1,670
  • PDF: 539
  • XML: 67
  • Total: 2,276
  • Supplement: 102
  • BibTeX: 47
  • EndNote: 54
Views and downloads (calculated since 21 Nov 2022)
Cumulative views and downloads (calculated since 21 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,276 (including HTML, PDF, and XML) Thereof 2,161 with geography defined and 115 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Read more