Women Researcher Award
Marzieh Mokarram
Shiraz University, Iran
The Women Researcher Award recognizes the academic and scientific contributions of Marzieh Mokarram of Shiraz University, Iran, whose research activities are centered on environmental science, land-surface processes, remote sensing, machine learning, soil erosion assessment, desert dynamics, and environmental monitoring. Her scholarly record reflects sustained engagement with interdisciplinary approaches that combine geospatial information, satellite observations, environmental modelling, and data-driven analytical methods.[1]
Abstract
Marzieh Mokarram is an environmental science researcher affiliated with Shiraz University. Her research profile demonstrates contributions to environmental assessment through remote sensing, geographical information systems, machine learning, land degradation analysis, soil erosion prediction, aerosol studies, and water-quality monitoring. Her recent publications address contemporary environmental challenges through quantitative and interdisciplinary methodologies.[2]
Keywords
Environmental Science; Remote Sensing; Soil Erosion; Machine Learning; Desert Dynamics; Water Quality; Geospatial Analysis.
Introduction
Environmental research increasingly relies on integrated observational and computational methods to understand land degradation, climate interactions, water systems, and ecosystem change. Mokarram’s research interests align with this multidisciplinary direction by applying geospatial datasets and advanced modelling techniques to environmental problems. Her work contributes to the development of analytical frameworks for interpreting complex relationships between environmental variables across regional and global scales.[3]
Research Profile
With 142 indexed documents, 2,389 citations, and an h-index of 25 according to the supplied Scopus research indicators, Mokarram maintains an established publication profile within environmental science. Her work spans land-surface monitoring, environmental mapping, erosion susceptibility, machine learning applications, and satellite-based environmental assessment.[1]
Research Contributions
- Application of machine learning methods for soil erosion risk mapping and environmental prediction.
- Investigation of desert variations and effective water availability at global scales.
- Analysis of mineral aerosols and industrial influences on lake water quality using spectral indices.
- Integration of multi-sensor remote sensing and deep neural networks for environmental assessment.
Publications
Recent scholarly outputs include research on the Desert Model Intercomparison Project benchmark framework, machine learning approaches to soil erosion risk mapping, global desert variations associated with water availability, mineral aerosol effects on lake water quality, and automatic prediction of soil erosion in arid regions. These studies illustrate continued engagement with environmental monitoring and computational analysis.[2][4]
Research Impact
The citation record and h-index indicate measurable scholarly visibility across environmental science publications. The research portfolio contributes to evidence-based understanding of land and water systems while demonstrating the value of machine learning and remote sensing for environmental decision support.[1]
Award Suitability
The Women Researcher Award is suitable for recognizing Mokarram’s sustained research activity, publication record, interdisciplinary methodology, and contributions to environmental science. Her work demonstrates continued engagement with contemporary scientific challenges involving land degradation, water resources, atmospheric processes, and geospatial technologies.
Conclusion
Marzieh Mokarram’s academic profile represents a sustained contribution to environmental research through geospatial analysis, remote sensing, and computational modelling. Her publication activities and research indicators provide a basis for academic recognition under the Women Researcher Award category at the International Forensic Scientist Awards.
External Links
References
- Elsevier. (n.d.). Scopus author details: Marzieh Mokarram, Author ID 49964190700. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=49964190700
- Mokarram, M. et al. (2026). Machine Learning Approaches to Soil Erosion Risk Mapping: A Comparison between Logistic Regression and Fast Large Margin. Journal of the Civil Engineering Forum.
https://doi.org/10.22146/jcef.24796
- Mokarram, M. et al. (2026). Global Desert Variations During 1985–2024 Associated With Effective Water Availability. Geophysical Research Letters.
https://doi.org/10.1029/2025GL120826
- Mokarram, M. et al. (2026). Global analysis of mineral aerosol and industrial effects on lake water quality using spectral indices and machine learning. Water Research.
https://doi.org/10.1016/j.watres.2025.125055
- Mokarram, M. et al. (2026). Automatic prediction of soil erosion in arid regions using multi-sensor remote sensing integrated with object-based image analysis and deep neural networks. Environmental Earth Sciences.
https://doi.org/10.1007/S12665-026-12973-7
- European Geosciences Union. (2026). Desert Model Intercomparison Project benchmark framework version 1.0 for assessing land-surface dynamics and surface memory in monthly dust aerosol optical depth over North Africa. Egusphere Preprint.
https://doi.org/10.5194/egusphere-2026-2271
