Yaggesh Kumar Sharma

About

Dr. Yaggesh Kumar Sharma is a researcher specializing in hydrology, remote sensing, geospatial analysis, machine learning, and environmental data science. His research focuses on developing data-driven approaches for groundwater monitoring, hydrological prediction, drought assessment, and sustainable water-resource management, particularly under data-scarce and uncertain conditions.
His expertise includes groundwater hydrology, Earth Observation, GIS, artificial intelligence, spatio-temporal modelling, and uncertainty assessment. He has worked extensively with satellite observations, land-surface datasets, and ground-based hydrological measurements to develop approaches for groundwater reconstruction and forecasting, terrestrial water-storage assessment, recharge estimation, and climate-related water-resource analysis.
He has also contributed to multi-hazard risk assessment and geospatial modelling, covering floods, earthquakes, landslides, cyclones, and other environmental hazards. His research has been published in international peer-reviewed journals and presented at scientific conferences. His broader interests focus on developing AI- and Earth Observation-based solutions for groundwater, drought, climate resilience, and sustainable water-resource management.

Employment

• International Water Management Institute Intern
  2024 - 2024
• Asian Disaster Preparedness Center Project Intern
  2021 - 2021
• RMSI Private Limited GIS Engineer
  2019 - 2021
• ESRI India Intern
  2018 - 2018
• Uttarakhand Government Intern
  2018 - 2018
• National Institute of Hydrology Intern
  2017 - 2017

Education

• Kyung Hee University Ph.D.
  2023 - Present
• Asian Institute of Technology Master's of Engineering
  2020 - 2022
• University of Petroleum and Energy Studies Bachelor's of Technology
  2015 - 2019

Projects & Funding

Projects & funding information is unavailable.

Publications (8)

• A computational framework for basin-scale pond recharge estimation using multi-source geospatial data
  Computers & Geosciences 2026 DOI: 10.1016/j.cageo.2026.106232
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• A unified framework for groundwater level imputation and forecasting in data-limited catchments
  Journal of Hydrology 2026 DOI: 10.1016/j.jhydrol.2026.135944
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• A Systematic Literature Review of MODFLOW Combined with Artificial Neural Networks (ANNs) for Groundwater Flow Modelling
  Water 2025 DOI: 10.3390/w17162375
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• Enhancing Aquifer Reliability and Resilience Assessment in Data-Scarce Regions Using Satellite Data: Application to the Chao Phraya River Basin
  Remote Sensing 2025 DOI: 10.3390/rs17101731
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• Strategic imputation of groundwater data using machine learning: Insights from diverse aquifers in the Chao-Phraya River Basin
  Groundwater for Sustainable Development 2025 DOI: 10.1016/j.gsd.2024.101394
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• Building Vulnerability Assessment using Artificial Intelligence forLandslide Susceptibility Zone in Champawat District, India
  EGU 2023 2023 DOI: 10.5194/egusphere-egu23-1957
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• Deterministic Seismic Hazard Assessment by revisiting 1991 Uttarkashi and 1999 Chamoli Earthquake for Uttarakhand, India
  2023 DOI: 10.5194/egusphere-egu23-11563
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• Development of Flow Duration Curves and Eco-Flow Metrics for the Tawi River Basin - (Jammu, India)
  International Journal of Advanced Remote Sensing and GIS 2019 DOI: 10.23953/cloud.ijarsg.432
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