Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 9
Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3087-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3087-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 9
Research article
 | 
05 Sep 2025
Research article |  | 05 Sep 2025

Evaluating Yangtze River Delta Urban Agglomeration flood risk using a hybrid method of automated machine learning and analytic hierarchy process

Yu Gao, Haipeng Lu, Yaru Zhang, Hengxu Jin, Shuai Wu, Yixuan Gao, and Shuliang Zhang

Viewed

Total article views: 1,182 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
460 166 556 1,182 46 54
  • HTML: 460
  • PDF: 166
  • XML: 556
  • Total: 1,182
  • BibTeX: 46
  • EndNote: 54
Views and downloads (calculated since 16 Sep 2024)
Cumulative views and downloads (calculated since 16 Sep 2024)

Viewed (geographical distribution)

Total article views: 1,182 (including HTML, PDF, and XML) Thereof 1,136 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Sep 2025
Download
Short summary
This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA), where we determined flood risk assessment indices across different dimensions, including hazard, exposure, vulnerability, and resilience. We constructed a flood risk assessment model using automated machine learning and the analytic hierarchy process to examine the spatial and temporal changes in flood risk in the region over the past 30 years (1990 to 2020), aiming to provide a scientific basis for flood prevention and resilience strategies in the YRDUA.
Read more
Share
Altmetrics
Final-revised paper
Preprint