Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 7
Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-1925-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1925-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 7
Development and technical paper
 | 
06 Apr 2023
Development and technical paper |  | 06 Apr 2023

A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA

Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki

Viewed

Total article views: 1,216 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
874 283 59 1,216 55 49 48
  • HTML: 874
  • PDF: 283
  • XML: 59
  • Total: 1,216
  • Supplement: 55
  • BibTeX: 49
  • EndNote: 48
Views and downloads (calculated since 21 Oct 2022)
Cumulative views and downloads (calculated since 21 Oct 2022)

Viewed (geographical distribution)

Total article views: 1,216 (including HTML, PDF, and XML) Thereof 1,144 with geography defined and 72 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Dec 2024
Download
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Read more