Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 10
Articles | Volume 26, issue 10
https://doi.org/10.5194/nhess-26-5119-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-5119-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 10
Research article
 | 
20 Oct 2026
Research article |  | 20 Oct 2026

Hydrologically enhanced machine learning framework for urban flood inundation mapping using multi-sensor remote sensing data: a case study of Mumbai, India

Ankush S. Pawar and Gayatri M. Phade

Viewed

Total article views: 1,546 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,089 345 112 1,546 96 89
  • HTML: 1,089
  • PDF: 345
  • XML: 112
  • Total: 1,546
  • BibTeX: 96
  • EndNote: 89
Views and downloads (calculated since 22 Apr 2026)
Cumulative views and downloads (calculated since 22 Apr 2026)

Viewed (geographical distribution)

Total article views: 1,546 (including HTML, PDF, and XML) Thereof 1,507 with geography defined and 39 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 11 Nov 2026
Download
Short summary
Urban flooding can disrupt homes, transport,essential services, creating a need for timely and reliable mapping. This study develops a machine learning approach for mapping flood inundation in Mumbai using satellite observations, elevation, rainfall, and river-related data. Results show that combining these sources improves flood classification and helps identify areas with greater flood probability. Approach supports informed flood risk assessment and future decision-support applications.
Read more
Share
Altmetrics
Final-revised paper
Preprint