Dr. Sushindra Kumar Gupta

About

Dr. Sushindra Kumar Gupta is a Scientist-C in the Groundwater Hydrology Division at the National Institute of Hydrology (NIH), Roorkee, Uttarakhand. He did his B.Tech. from Gautam Buddha Technical University, Lucknow, in 2011, M.Tech. in Soil and Water Conservation Engineering from Banaras Hindu University, Varanasi in 2013 and Ph.D. from MNIT, Jaipur in 2019. The major areas of his research are Artificial Intelligence, Machine learning, Rainfall-Runoff Modeling, Soil Conservation Service Curve Number (SCS-CN) Method, Sediment Transport Modeling, Soil Moisture Accounting, Remote Sensing & GIS application in Water Resources Engineering, and Optimization Techniques at various scales (e.g., natural, watershed-scale) across multiple watersheds.

Employment

• National Institute of Hydrology Scientist 'C'
  2024 - Present
• National Institute of Hydrology Roorkee Ph.D.
  ? - 2020

Education

• Malaviya National Institute of Technology Jaipur Research Scholar
  2014 - 2019

Projects & Funding

Projects & funding information is unavailable.

Publications (13)

• Closure to “Comparative Evaluation of Machine-Learning Models for Predicting Daily Evapotranspiration in a Naturally Ventilated Greenhouse”
  Journal of Irrigation and Drainage Engineering 2026 DOI: 10.1061/jidedh.ireng-10819
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• Development of soil surface wetness models using machine learning techniques in the selected sites in Punjab, North-Western India
  Scientific Reports 2026 DOI: 10.1038/s41598-026-50687-9
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• Comparative Evaluation of Machine-Learning Models for Predicting Daily Evapotranspiration in a Naturally Ventilated Greenhouse
  Journal of Irrigation and Drainage Engineering 2025 DOI: 10.1061/jidedh.ireng-10441
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• Prediction of water quality and LULC analysis using machine learning and geospatial techniques
  Water Practice & Technology 2025 DOI: 10.2166/wpt.2024.310
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• A Deep Learning Approach to Predict Surface Soil Wetness and Its Uncertainty Analysis Over the Tel River Basin, India
  CLEAN – Soil, Air, Water 2025 DOI: 10.1002/clen.70003
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• Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA
  The Springer Series in Applied Machine Learning 2025 DOI: 10.1007/978-3-031-64403-0_8
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• Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India
  Physics and Chemistry of the Earth, Parts A/B/C 2024 DOI: 10.1016/j.pce.2024.103603
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• Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP)
  Hydrological Processes 2020 DOI: 10.1002/hyp.13789
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• An Event-Based Sediment Yield and Runoff Modeling Using Soil Moisture Balance/Budgeting (SMB) Method
  Water Resources Management 2019 DOI: 10.1007/s11269-019-02329-1
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• Soil Moisture Accounting (SMA)based sediment graph models for small watersheds
  Journal of Hydrology 2019 DOI: 10.1016/j.jhydrol.2019.04.077
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