Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4299-2025
https://doi.org/10.5194/nhess-25-4299-2025
Research article
 | 
04 Nov 2025
Research article |  | 04 Nov 2025

Hydrological drought prediction and its influencing features analysis based on a machine learning model

Min Li, Yuhang Yao, Zilong Feng, and Ming Ou

Viewed

Total article views: 3,295 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,293 901 101 3,295 80 104
  • HTML: 2,293
  • PDF: 901
  • XML: 101
  • Total: 3,295
  • BibTeX: 80
  • EndNote: 104
Views and downloads (calculated since 25 Jun 2025)
Cumulative views and downloads (calculated since 25 Jun 2025)

Viewed (geographical distribution)

Total article views: 3,295 (including HTML, PDF, and XML) Thereof 3,219 with geography defined and 76 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
This study proposes an innovative method for predicting drought in the Huaihe River Basin of China using advanced machine learning and interpretable artificial intelligence techniques. By analyzing more than 50 years of data, the model successfully predicted four drought categories with an accuracy of 79.9 %. It used explanatory methods to analyze the contribution of different drought influencing factors, providing key insights for early warning systems and water resources planning.
Share
Altmetrics
Final-revised paper
Preprint