Articles | Volume 26, issue 10
https://doi.org/10.5194/nhess-26-5119-2026
https://doi.org/10.5194/nhess-26-5119-2026
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

Cited articles

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Amitrano, D., Di Martino, G., Guida, R., Iervolino, P., Iodice, A., Papa, M. N., Riccio, D., and Ruello, G.: Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications, Remote Sensing, 13, 604, https://doi.org/10.3390/rs13040604, 2021. 
Chini, M., Hostache, R., Giustarini, L., and Matgen, P.: A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case, IEEE T. Geosci. Remote, 55, 6975–6988, https://doi.org/10.1109/TGRS.2017.2737664, 2017. 
Choné, J. C., Biron, P. M., Buffin-Bélanger, T., Mazgareanu, I., Neal, J. C., and Sampson, C. C.: An assessment of large-scale flood modelling based on LiDAR data, Hydrol. Process., 35, e14333, https://doi.org/10.1002/hyp.14333, 2021. 
Islam, T., Zeleke, E. B., Afroz, M., and Melesse, A. M.: A systematic review of urban flood susceptibility mapping: remote sensing, machine learning, and other modeling approaches, Remote Sens., 17, 524, https://doi.org/10.3390/rs17030524, 2025. 
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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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