Machine Learning-Based Assessment of High-Impact Low Likelihood Precipitation Events in North India

Aayushi Tandon; Amit Awasthi; Kanhu Charan Pattnayak

doi:https://doi.org/10.5194/egusphere-egu25-14838

[Back] [Session HS4.10]

EGU25-14838, updated on 15 Mar 2025

https://doi.org/10.5194/egusphere-egu25-14838

EGU General Assembly 2025

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

Machine Learning-Based Assessment of High-Impact Low Likelihood Precipitation Events in North India

Aayushi Tandon¹, Amit Awasthi¹, and Kanhu Charan Pattnayak²

Aayushi Tandon et al.

¹School of Advanced Engineering, University of Petroleum and Energy Studies, Dehradun, India (tdn2408aayushi@gmail.com)
²School of Earth and Environment, University of Leeds, Leeds, United Kingdom

How to cite: Tandon, A., Awasthi, A., and Pattnayak, K. C.: Machine Learning-Based Assessment of High-Impact Low Likelihood Precipitation Events in North India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14838, https://doi.org/10.5194/egusphere-egu25-14838, 2025.

This abstract has been withdrawn on 25 Jul 2025.