Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5605-2022
https://doi.org/10.5194/essd-14-5605-2022
Data description paper
 | 
20 Dec 2022
Data description paper |  | 20 Dec 2022

WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting

Ibrahim Demir, Zhongrun Xiang, Bekir Demiray, and Muhammed Sit

Viewed

Total article views: 2,677 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,857 730 90 2,677 68 72
  • HTML: 1,857
  • PDF: 730
  • XML: 90
  • Total: 2,677
  • BibTeX: 68
  • EndNote: 72
Views and downloads (calculated since 02 Mar 2022)
Cumulative views and downloads (calculated since 02 Mar 2022)

Viewed (geographical distribution)

Total article views: 2,677 (including HTML, PDF, and XML) Thereof 2,477 with geography defined and 200 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 15 Jul 2024
Download
Short summary
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological modeling. This dataset is designed to support cutting-edge deep learning studies for a more accurate streamflow forecast model. We also propose a modeling task for comparative model studies and provide sample models with codes and results as the benchmark for reference. This makes up for the lack of benchmarks in earth science research.
Altmetrics
Final-revised paper
Preprint