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

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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.
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