Development of a Multi-Hazard Index for India: Applying CNN U-net Deep Learning framework to major Hydro-Meteorological Extremes

Rachit Rachit; Mohit Prakash Mohanty; Ashish Pandey; Anil Kumar Gupta

doi:https://doi.org/10.5194/egusphere-egu26-6401

[Back] [Session NH10.1]

EGU26-6401, updated on 10 Apr 2026

https://doi.org/10.5194/egusphere-egu26-6401

EGU General Assembly 2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of a Multi-Hazard Index for India: Applying CNN U-net Deep Learning framework to major Hydro-Meteorological Extremes

Rachit Rachit¹, Mohit Prakash Mohanty¹, Ashish Pandey¹, and Anil Kumar Gupta^2,1

Rachit Rachit et al.

¹Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India
²Integrated Centre for Adaptation to Climate Change, Disaster Risk Resilience and Sustainability (ICARS), Indian Institute of Technology Roorkee - Greater Noida Extension Centre, Greater Noida, India

How to cite: Rachit, R., Mohanty, M. P., Pandey, A., and Gupta, A. K.: Development of a Multi-Hazard Index for India: Applying CNN U-net Deep Learning framework to major Hydro-Meteorological Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6401, https://doi.org/10.5194/egusphere-egu26-6401, 2026.

This abstract has been withdrawn after no-show on 11 Aug 2026.