Nikunj K. Mangukiya
National University of Singapore, Indian Institute of Technology Roorkee, Central Salt and Marine Chemicals Research Institute
0000-0001-8132-6958
About
I am a Postdoctoral Research Fellow at the National University of Singapore (NUS), Singapore. I recently completed my Ph.D. in Hydrology from the Department of Hydrology, Indian Institute of Technology Roorkee, where my research focused on developing deep learning-based frameworks for hydrological predictions. My work addressed key challenges such as data sparsity, temporally discrete observations, physical interpretability in regulated river systems, and flood risk assessment in ungauged watersheds.
My current research continues to explore the integration of artificial intelligence and hydrological sciences, with a particular focus on data-driven and physics-informed modeling, climate change impacts, and flood hazard mapping. I am deeply passionate about advancing scientific understanding and fostering interdisciplinary collaborations to solve real-world water challenges.
Employment
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National University of Singapore Postdoctoral Research Fellow2025 - Present
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Indian Institute of Technology Roorkee PhD Research Scholar2021 - 2025
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Central Salt and Marine Chemicals Research Institute Project Associate - I2021 - 2021
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Shree Dhanvantry College of Diploma Engineering Lecturer2017 - 2017
Education
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Indian Institute of Technology Roorkee PhD2021 - 2025
Projects & Funding
Projects & funding information is unavailable.
Publications (19)
- Integrating Analytical Hierarchy Process and Machine Learning for Enhancing Flood Hazard Mapping Save
- Introducing a Network of Experimental Catchments in the Urban Tropics Save
- Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? Save
- Benchmarking and Selecting Optimal Hydrological Models for Large‐Sample Applications Considering Complexity and Uncertainty Save
- Integrating Reservoir Dynamics Into Differentiable Process‐Based Hydrological Model for Enhanced Streamflow Estimation Save
- CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India Save
- Deep Learning‐Based Approach for Enhancing Streamflow Prediction in Watersheds With Aggregated and Intermittent Observations Save
- Machine learning-based regional flood frequency analysis of Indian watersheds Save
- Alternate pathway for regional flood frequency analysis in data-sparse region Save
- A novel multi-model ensemble framework for fluvial flood inundation mapping Save