Norwegian version of this page

Pragay Shourya Moudgil

Doctoral Research Fellow - Section of Physical geography and Hydrology
Image of Pragay Shourya Moudgil
Email p.s.moudgil@geo.uio.no
Mobile phone +47 40300054
Room 315b
Username
Visiting address Sem Sælands vei 1 Geologibygningen 0371 Oslo
Postal address Postboks 1047 Blindern 0316 Oslo
Other affiliations Faculty of Mathematics and Natural Sciences (Student) The International Summer School (Student)

Academic interests

A Civil Engineer who transitioned into the field of geoscience, with a particular focus on Glaciers. Presently, I am actively involved in the implementation of ML techniques on global-scale glaciers under Prof. Regine Hock’s group. My research interests are:

  • Machine Learning/Deep Learning
  • Glaciology
  • Hydrology
  • Satellite Remote Sensing

Background

  • 2024 – Present: PhD Research Fellow under ERC-funded GLACMASS project at the Department of Geosciences, University of Oslo, Norway.
    • Theme: Machine Learning-based Global Glacier modelling.
  • 2021 – 2023: M.Tech Earthquake Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad.
    • Thesis: Filling temporal gaps in GRACE/GRACE-FO using Deep Learning.
  • 2017 – 2021: B.Tech Civil Engineering, National Institute of Technology, Hamirpur.
    • Thesis: 2D Unsteady flow modelling through HEC-RAS.
Tags: Machine Learning & Deep Learning, Glaciology, Hydrology, Remote sensing

Publications

Publications 2023

  • Moudgil, P. S., & Rao, G. S. (2023). Groundwater levels estimation from GRACE/GRACE-FO and hydro-meteorological data using deep learning in Ganga River basin, India. Environmental Earth Sciences, 82(19), 441.
  • Moudgil, P.S., Rao, G.S. & Heki, K. Bridging the Temporal Gaps in GRACE/GRACE–FO Terrestrial Water Storage Anomalies over the Major Indian River Basins Using Deep Learning. Nat Resour Res 33, 571–590 (2024). https://doi.org/10.1007/s11053-024-10312-w

Conferences

  • Moudgil, P.S., Dhyey Dabhi, and Rao. G.S. “Predicting Groundwater Level Changes in Northern India from GRACE and GRACE Follow-On Data using Deep Learning Algorithms.” Fall Meeting 2022. AGU.  (Online poster).
  • Moudgil, P. S., and Rao, G. S. “Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Changes Over Indian Sub-Continent using Deep Learning.”, EGU April 2022 (Online oral presentation).
  • Moudgil, P.S., and Rao, G.S. “Predicting Groundwater Level Changes in Northern India from GRACE and GRACE Follow-On Data using CNN.” In  “National Seminar on Machine Learning and Deep Learning Application in Geosciences” Seminar at IIT-Bombay April 2022 (Oral presentation).
  • Moudgil, Pragay Shourya; Rao, G. Srinivasa & Heki, Kosuke (2024). Bridging the Temporal Gaps in GRACE/GRACE–FO Terrestrial Water Storage Anomalies over the Major Indian River Basins Using Deep Learning. Natural Resources Research. ISSN 1520-7439. 33(2), p. 571–590. doi: 10.1007/s11053-024-10312-w. Full text in Research Archive

View all works in NVA

  • Moudgil, Pragay Shourya; Vaate, Inger Bij de; Hock, Regine & Guillet, Gregoire (2025). Mass balance modeling of glaciers in Switzerland using machine learning. Full text in Research Archive

View all works in NVA

Published Jan. 30, 2024 9:45 AM - Last modified Oct. 14, 2025 4:19 PM

Projects