Predictive understanding of socioeconomic flood impact in data-scarce regions based on channel properties and storm characteristics: application in High Mountain Asia (HMA)

Khanam, Mariam; Sofia, Giulia; Rodriguez, Wilmalis; Nikolopoulos, Efthymios I.; Lu, Binghao; Song, Dongjin; Anagnostou, Emmanouil N.

doi:https://doi.org/10.5194/nhess-25-3759-2025

Articles | Volume 25, issue 10

https://doi.org/10.5194/nhess-25-3759-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Hydro-meteorological extremes and hazards: vulnerability,...

https://doi.org/10.5194/nhess-25-3759-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 10

Research article

|

06 Oct 2025

Research article |

| 06 Oct 2025

Predictive understanding of socioeconomic flood impact in data-scarce regions based on channel properties and storm characteristics: application in High Mountain Asia (HMA)

Mariam Khanam, Giulia Sofia, Wilmalis Rodriguez, Efthymios I. Nikolopoulos, Binghao Lu, Dongjin Song, and Emmanouil N. Anagnostou

Data sets

High Mountain Asia 30m and 8m Flood Geomorphic Potential (HMA2_FGP, Version 1). G. Sofia, M. Khanam, E. Nikolopoulos, and E. N. Anagnostou https://doi.org/10.5067/WS83XMWHGEM9

Short summary

This study comprehends and predicts the socioeconomic effects of floods in the High Mountain Asia (HMA) region. We proposed a machine learning strategy for mapping socioeconomic flood damage. We predicted the life year index (LYI), which quantifies the financial cost and loss of life caused by floods, using variables including climate, geomorphology, and population. The study's overall goal is to offer useful information on flood susceptibility and subsequent risk mapping in the HMA region.