Research Assistant Professor
Department of Civil and Environmental Engineering,School of Engineering and Computing, University of South Carolina.
The Carolinas Integrated Sciences & Assessments (CISA; a NOAA-funded research center), University of South Carolina.
Hydro-environmental Research Centre (HRC),School of Engineering, Cardiff University, United Kingdom.
Water Engineering and Sciences. 2009
University of Tehran, Iran. 2002
-GIS in Civil and Environmental Engineering (proposed course-undergraduate level)
-GIS in Water Resources Engineering (proposed course-graduate level)
-Research in Civil Engineering
-Selected Topics in Civil Engineering
-Thesis Preparation in Civil Engineering
-Thesis Preparation in Computer Engineering (UofSC & Clemson University)
-Dissertation Preparation in Civil Engineering
-River Basin Management (Cardiff University & UofSC)
-Stochastic Hydrology and Hydroinformatics (proposed course-graduate & undergraduate levels)
-Advanced Hydrology (graduate level;co-taught with prof. Meadows of UofSC)
-Senior Design in Civil Engineering (co-taught with prof. Meadows of UofSC)
Dr. Samadi is trained as a water resources engineer and works to advance the field of hydroinformatics where data and computational programs are used to improve the understanding, forecasting, and management of water resources systems. Much of her current works are focused on Flood Computational Modeling and Forecasting, Impacts of Flooding on Critical Infrastructure, Smart Cities and Infrastructure, Big Data Analytics, and Geographic Information System (GIS). Research sponsors include the National Science Foundation (NSF), Savannah River National Lab, NOAA Sea Grant Consortium, and South Carolina Department of Transportation (SCDOT). Dr. Samadi is a founding member of Women in Artificial Intelligence (WomenInAI) that aims to strengthen the diversity and increase female representation and participation in AI and working towards gender-inclusive data analytics and modeling at academia. Vidya is highly committed to combat the disparity of women in Engineering and currently contributes to the Society of Women Engineers as well as Clemson PEER and WISE Inclusion programs to inspire future women Engineers.
PhD/MS applicants for Clemson: Please follow the instructions on the Clemson admissions page.
Academic Research Interest: Dr. Samadi' academic research focuses on developing cyber-physical modeling systems, an interdisciplinary approach combining hydrology, water resources engineering, computer science, and data analytics. The goal is to leverage advances in watershed modeling and computing and data science to address problems and challenges associated with water resources systems. Her team has developed many modeling systems including Artificial Intelligent and physical based models for surface water modeling systems.
Industry Research Interest: Vidya' industry research interest concentrates around GIS software development, web and mobile application development, and web programming that involve creating, leveraging and utilizing web mapping solutions to solve specific environmental problems, build complete applications, or consume or produce data and geospatial processing services. Web GIS programming applies to both mobile and desktop application development. Dr. Samadi is proficient in various programming languages (python, Java, HTML, etc.) and seeks collaboration with academics and industry to develop tools that can address applied engineering research and practical settings.
Awards & Honors (selected)
1. Clemson Support for Early Exploration and Development Award. 2021. Clemson University.
2. Advanced Support for Innovative Research Excellence Award. 2017. The University of South Carolina.
3. Outstanding Reviewer Award.2016. ASCE Journal of Hydrologic Engineering.
4. Second ranked Nationwide M.Sc. Competition in Water Engineering. 2000. Iranian Ministry of Sciences and Research.
Professional Services (selected)
1. Chair. The CUAHSI Informatics Committee, 2021-2022.
2. Member. The World Meteorological Organization (WMO)-GEWEX Hydroclimatology Panel, 2019-2022.
3. Associate Editor. Hydrological Sciences Journal. 2020-2022.
Extension and Outreach
Dr. Samadi is an Associate Editor for Hydrological Sciences Journal and also serves CUAHSI Informatics committee. She is also a member of WMO-GEWEX Hydroclimatology panel. At the state level, Dr. Samadi leads South Carolina surface water modeling systems for SC Water Resources Center. She is affiliated with the SC Water Resources Center and works collaboratively with state stakeholders as well as 12 water agents (of the 12 current water agents, 11 of them are female) across the state of South Carolina to address water education and outreach. In addition, Dr. Samadi collaborates with extension professionals to enrich a youth water resources education program across the state (4-H2O; K-12 population) as well as with the Hunnicutt Creek stream restoration team and the Intelligent River® initiative (funded by NSF) for surface water modeling activities.
* Denotes graduate student under my supervision.
Research Paper (selected)
1. Donratanapat, N.*, Samadi S., Vidal, M.J., S. Sadeghi Tabas*. 2020. A National Scale Big Data Analytics Pipeline to Assess the Potential Impacts of Flooding on Critical Infrastructures and Communities. Environmental Modelling & Software.DOI: https://doi.org/10.1016/j.envsoft.2020.104828
2. Samadi S., Pourreza-Bilondi M., Wilson C. A. M.E., Hitchcock, B.D. 2020. Bayesian Model Averaging with Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling. AGU Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2019MS001924.
3.Rezaei-Balf, M., Rajabtabar, M., Samadi, S., Ghaemi, A., Shamshirband, Sh. 2020. An Integrated Machine Learning, Noise Suppression, and Population-based Algorithm to Improve Total Dissolved Solids prediction. Engineering Applications of Computational Fluid Mechanics. Accepted.
4. Liu H.*, Hitchcock D., and Samadi S. 2020. Spatio-temporal analysis of flood data from South Carolina. Journal of Statistical Distributions and Applications. 7, 11 (2020). https://doi.org/10.1186/s40488-020-00112-x.
5. Rezaie-balf M., Fani Nowbandegani S., Samadi S., Fallah H., Alaghmand S. 2019. An Ensemble Decomposition-based Artificial Intelligence Approaches for Daily Streamflow Prediction. Water Journal - 11(4), 709; https://doi.org/10.3390/w11040709.
6. Philips, R. C.*, Samadi, S., Meadows, M.E. 2018. How extreme was the October 2015 flood in the Carolinas? An assessment of flood frequency analysis and distribution tails. Journal of Hydrology. DOI: https://doi.org/10.1016/j.jhydrol.2018.05.035.
7. Samadi S., Tufford D., Carbone, G. 2018. Estimating Hydrologic Model Uncertainty in the Presence of Complex Residual Error Structures- Stochastic Environmental Research and Risk Assessment. 32: pp 1259–128, 2018. DOI: 10.1007/s00477-017-1489-6.
8. Samadi S., Tufford D., Carbone, G. 2017. Assessing prediction uncertainty of a semi-distributed hydrology model for a shallow aquifer dominated environmental system– Journal of the American Water Resources Association (JAWRA)- special issue on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. 53(6): 1368-1389. https://doi.org/10.1111/1752-1688.12596.
9. Samadi S., Meadows M.E. 2017. The Transferability of Terrestrial Water Balance Components under Uncertainty and Non-stationarity: A Case Study of the Coastal Plain Watershed in the Southeastern United States. River Research and Applications. 33:796–808.
10. Samadi S. 2016. Assessing the Sensitivity of SWAT Physical Parameters to Potential Evapotranspiration Estimation Methods over a Coastal Plain Watershed in the Southeast United States. Hydrology Research, in press. DOI: 10.2166/nh.2016.034.
11. Pourreza-Bilondi Mohsen, Samadi S., Akhoond-Ali Ali-Mohammad, Ghahraman Bijan. 2016. On the Assessment of Reliability in Semiarid Convective Flood Modeling Using Bayesian Framework. ASCE Journal of Hydrologic Engineering, 05016039:1-16.
12. Sadeghi-Tabas S.*, Samadi S., Akbarpour A., Pourreza Bilondi M. 2016. Sustainable Groundwater Modeling Using Single and Multi-Objective Optimization Algorithms. Journal of Hydroinformatics. DOI: 10.2166/hydro.2016.006.
11. Etemadi H.*, Samadi S., Sharifikia M. 2014. Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Climate Dynamics. 42:2899-2920.
1. B. Toni. Modern Statistical Methods for Spatial and Multivariate Data. In: David Hitchcock, Haigang Liu, and S. Samadi. 2019. Spatial and spatial-temporal analysis: A case study based on precipitation data from South Carolina. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Vol. Netherlands: Springer.
Conference Contribution (selected)
1.S. Samadi, N. Donratanapat, S. Sadeghi Tabas*, A. Apon, C. Post. 2020. Estimating Critical Infrastructure Inundation Levels Using Crowdsourced Images and AI-driven Data Analytics. The 10th International Conference on Environmental Modelling and Software - Modelling for Environmental Sustainability,Brussel, Belgium.
2. Samadi, V, Donratanapat, N. *, Vidal, M.J. 2019. A National-scale Big Data Pipeline For Flood Emergency Response and Management. NSF UFOKN Workshop, Raleigh, NC, (invited).
3. Sadeghi Tabas S.*, Samadi V. 2019. Real-Time Coastal Flood Prediction Using Machine Learning Approach. The American Geophysical Union. 9-13 December 2019. San Francisco, California.
4. Philips, R.*, Samadi, V. 2019. Extreme Flooding: A Non-Stationary Hydrologic Assessment of Seasonal Floods in South Carolina. The American Geophysical Union. 9-13 December 2019. San Francisco, California.
5. Samadi, S., Philips, R.*, Meadows, M.E. 2019. Compound Flood Impacts on Transportation System During Hurricane Irma. Transportation Resilience conference. Washington, D.C. Nov. 2019.
6. Phillips R. *, Samadi S., and Meadows, M.E. 2018. Non-Stationary Flood Frequency Analysis in the Carolinas in response to the October 2015 Flooding. South Carolina Water Resources Conference. Oct. 2018.
1.Donratanapat, N.*, Samadi S. 2020. A Google Module for Computing Rapid Surface Runoff (funded by NSF). Released strictly using the MIT license.
2. Sadeghi Tabas, S.*, Samadi S. 2020. A Deep Learning Application for Managing Water Resources Systems (funded by NSF; will be Beta-tested in 2021).
3. Donratanapat, N.*, Samadi S., and Vidal, J. 2019. “FAIS”: Flood Analytics Information System (funded by NSF).Released strictly using the MIT license.
5. Philips R.*, Samadi S., Meadows M.E. 2018. Compound Flood Risk Analytics and Design Tool. R Package.
6. Liu H. *, Hitchcock D., and Samadi S. 2018. A tree-based model and spatial information for stochastic analysis of hydrological extremes. Python tool.