C.V. Raman Global University (CGU), Bhubaneswar | India
Dr. Abhijeet Das is an accomplished water resource engineer specializing in watershed hydrology, hydrological modeling, water quality control, and climate change impact assessment. He holds a B.Tech in Civil Engineering from ABIT College (BPUT, Rourkela), an M.Tech in Water Resource Engineering from the College of Engineering and Technology (BPUT, Bhubaneswar), and a Ph.D. in Water Resource Engineering from C.V. Raman Global University, Bhubaneswar. With extensive experience in GIS, remote sensing, and geoinformatics, he has contributed significantly to hydrological modeling, flood and drought assessment, and the integration of machine learning techniques for water resource management. Dr. Das has held positions as a GIS Analyst, Teaching Assistant, and Research Consultant, leading spatial data management, hydrological and hydraulic modeling, flood risk analysis, and multi-criteria decision support. His expertise spans numerical modeling, optimization techniques, artificial neural networks, and fuzzy logic for environmental and water systems. He has successfully guided GIS projects, developed dashboards for real-time monitoring, and collaborated with government agencies for watershed and crop management planning. Dr. Das’s research focuses on sustainable water management, climate resilience, and the Food-Energy-Water (FEW) nexus. His contributions have advanced environmental impact assessment and decision-making tools. He continues to drive innovation in water resource engineering and hydrological research, fostering a data-driven approach to sustainable resource management.
Featured Publications
Drinking water resources suitability assessment in Brahmani River, Odisha, based on pollution index of surface water utilizing advanced water quality methods. Scientific Reports.
An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment.
A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability.
Water quality assessment and geospatial techniques for the delineation of surface water potential zones: A data-driven approach using machine learning models. Desalination and Water Treatment
Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques. Desalination and Water Treatment