Abhijeet Das | Ecosystem Health | Next-Gen Science Innovation

Dr. Abhijeet Das | Ecosystem Health | Next-Gen Science Innovation 

C.V. Raman Global University, Bhubaneswar | India

Dr. Abhijeet Das has established a solid academic profile with contributions across water resources engineering, watershed hydrology, climate change impact assessment, and the integration of machine learning with geospatial sciences. His Scopus Author ID (57849079400) reflects a growing body of work indexed in reputed databases, comprising numerous conference papers, journal articles, book chapters, and edited volumes that collectively highlight his multi-disciplinary expertise. With a steadily increasing h-index, his research demonstrates measurable impact through cumulative citations, showcasing the relevance and recognition of his work in both national and international platforms. His scholarly output includes more than 50 documents spanning peer-reviewed journals, conferences, and collaborative projects, which together have garnered a significant number of citations, evidencing the utility of his findings in advancing water quality assessment, hydrological modeling, and environmental sustainability. In addition to publications, Dr. Das has co-edited and authored several books on civil engineering, research methodology, cancer biology, and artificial intelligence applications, further expanding the reach of his scholarship. His citation metrics underscore a consistent pattern of influence, where his research has been referenced by diverse groups of scholars and practitioners. This growing academic footprint, measured through documents, citations, and h-index, reflects his commitment to impactful research and global collaboration

Profiles: Scopus | Orcid

Featured Publications

An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance

Reimagining biofiltration for sustainable industrial wastewater treatment

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