Vidya Samadi, Ph.D., M.ASCE

Assistant Professor of Water Resources Engineering
Director of Clemson Hydrosystem and Hydroinformatics Research (HHR) Group
Agricultural Sciences Department

Personal Website:


 Educational Background

Research Assistant Professor in Water Resources Engineering
Department of Civil and Environmental Engineering,School of Engineering and Computing, University of South Carolina.

Postdoc in Water Resources Management
The Carolinas Integrated Sciences & Assessments (CISA; NOAA-funded research center), University of South Carolina.

Postdoc in Civil Engineering-Water Resources
Hydro-environmental Research Centre (HRC), Department of Civil Engineering, School of Engineering, Cardiff University, United Kingdom.

Ph.D. (D.Engr.)
Water Science and Engineering. 2009

M.S. (M.Engr.)
Water Engineering, University of Tehran, Iran. 2002

 Courses Taught

-Computational Methods in Water Resources (Clemson University)
-GIS in Civil and Environmental Engineering (proposed course-undergraduate level: UofSC)
-GIS in Water Resources Engineering (proposed course-graduate level: UofSC)
-Research in Civil Engineering (UofSC)
-Selected Topics in Civil Engineering (UofSC and Clemson University)
-Thesis Preparation in Civil Engineering (UofSC & Clemson University)
-Thesis Preparation in Agricultural Sciences-Informatics and Agricultural Water System Management (Clemson University)
-Thesis Preparation in Computer Science (UofSC & Clemson University)
-Master’s Thesis Research in Computer Science (Clemson University)
-Dissertation Preparation in Civil Engineering (UofSC & Clemson University)
-River Basin Management (Cardiff University & UofSC)
-Stochastic Hydrology and Hydroinformatics (proposed course-graduate & undergraduate levels; UofSC)
-Engineering Hydrology (UofSC)
-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 resource engineer and works to advance the field of hydroinformatics and cyber-physical modeling systems. Vidya's current research focuses on developing analytics and intelligent computing systems for water system modeling including stormwater/flood modeling, irrigation hydrologic modeling, and water system management. Her research has been continuously supported by various NSF programs (CBET, CMMI, CISE, OAC, GEO) as well as by other agencies such as USGS, USDA, Savannah River National Lab, NOAA, and the Department of Transportation (DOT), where she has served in the role of PI or the sole-PI for grants with a total of over $1.2M, and in the role of Co-PI for grants with a value of ~$1 M.

Dr. Samadi is a founding member of Women in Artificial Intelligence for Water (WomenInAI4Water) which aims to strengthen diversity and increase female representation and participation in AI and work towards gender-inclusive water data analytics and modeling in academia. She is highly committed to combating 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.

Ph.D./MS applicants for Clemson: Please follow the instructions on the Clemson admission page.

 Research Interests

Academic Research Interest: Dr. Samadi's academic research focuses on hydroinformatics and cyber-physical modeling systems, an interdisciplinary approach combining water resources engineering, computer science, and data analytics. The goal is to leverage advanced modeling and computing tools to address problems and challenges associated with water resource systems. Much of her current work is focused on machine learning applications in water resources and built environment domains, big data analytics, and hydroinformatics. Dr. Samadi's team has developed many packages and modeling systems, including Flood Analytics Information System (FAIS), FAIRDNN, Flood Image Classifier, Machine Learning for Irrigation Scheduling, Watershed Toolkit, and physical-based models.

Industry Research Interest: Dr. Samadi's industry research interest concentrates on GIS software development, web, and mobile application development, and web programming that involves creating, leveraging, and utilizing web mapping solutions to solve specific environmental problems, build complete applications, or consume or produce data and geospatial processing services. 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 water resources engineering research and practical settings.

Awards & Honors (selected)
1. The Universities Council on Water Resources (UCOWR) 2024 Mid-Career Award for Applied Research.
2. The American Society of Civil Engineers South Carolina section 2023 Technical Merit Award for "Flood Evaluation Tool" development.
3. Clemson Support for Early Exploration and Development Award. 2021. Clemson University.
4. 2016 Outstanding Reviewer Award. ASCE Journal of Hydrologic Engineering.
5. Ranked second in the National Entrance Exam competition for Water Engineering (MENG). 2000. Iranian Ministry of Science and Research.

Professional Services (selected)
1. Chair- The CUAHSI Standing Committee on Informatics, 2021-2023
2. Panel Member- The World Meteorological Organization (WMO)-GEWEX Hydroclimatology Panel (a global flood crosscutting project lead), 2019-2024
3. Board Member- International Environmental Modelling & Software Society, 2022-2024
4. Editor - Journal of Environmental Modeling & Software

 Extension and Outreach

Dr. Samadi serves on the WMO-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP). She currently leads a global, crosscutting, flood research initiative that allows GHP to propagate flood modeling and research knowledge from one region to the other and synthesizes results at the global scale. Also, she serves as a board member of the International Environmental Modelling and Software Society. At the national level, Dr. Samadi chairs the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Informatics Committee and oversees the CUAHSI informatics and data service activities, Informatics Blog, etc. At the state level, she works collaboratively with state stakeholders and other officials across the state of South Carolina to address water research and outreach.


Recent Research Papers (please refer to my Google Scholar for a full list of publications)
* Denotes graduate students under my supervision.
1. Sadeghi Tabas S.*, Samadi V. 2024. Fill-and-Spill: A Novel Deep Reinforcement Learning for Water Infrastructure Management and Control. ASCE Journal of Water Resources Planning and Management. In press.
2. Samadi V., Stephens, K., Hughes, A., Murray-Tuite, Pamela.2024. Challenges and Opportunities When Bringing Machines onto the Team: Human-AI Teaming and Flood Evacuation Decisions. Environmental Modelling & Software. In press.
3. Humaira, N.*, Samadi, S., Hubig, N. 2023. An end-to-end deep learning-based pipeline for real-time flood event classification and scene object detection from multimedia images. IEEE Access. In press.
4. Guido, B. I.*, Popescu, I., Samadi, V., and Bhattacharya, B.2023. An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States. The EGU Journal of Natural Hazards and Earth System Sciences. In press.
5. Windheuser L. *, Karanjit, R. *, Pally R. *, Samadi, S., and N.C. Hubig. 2023. An End-to-End Flood Gauge Height Prediction System Using Deep Neural Networks. The AGU Earth and Space Science, 10(1), p.e2022EA002385.
6. Phillips, R. C.*, Samadi, S., Hitchcock, B.D., Meadows, M., Wilson C. A. M.E. 2022. The devil is in the tail dependence: An assessment of multivariate copula-based frameworks and dependence concepts for coastal compound flood dynamics. The AGU Journal of Earth’s Future, p.e2022EF002705.
7. Sadeghi Tabas S.*, Samadi S. 2022. Variational Bayesian Dropout with a Gaussian Prior for Recurrent Neural Networks Application in Rainfall-Runoff Modeling. Environmental Research Letters.DOI:
8. Pally, R.*, Samadi S. 2022. Application of Image Processing and Convolutional Neural Networks for Flood Image Classification and Semantic Segmentation. Environmental Modelling & Software. 148, p.105285.
9. 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.133, p.104828.
10. 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. The AGU Journal of Advances in Modeling Earth Systems. 12(7), p.e2019MS001924.
11. 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:
12.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). 53(6): 1368-1389.
13.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.

Software Patents
1. Pally, R. *, Kranjit, R. *, Sadeghi Tabas, S*. Samadi, S., 2022. Image processing and semantic segmentation for flood image analytics and inundation mapping. Software/Copyright Disclosure - Tech ID: 2022-049
2. Donratanapat, N.*, Kranjit, R. *, Sadeghi Tabas, S*. Samadi, S., 2022. Flood Analytics Information System (FAIS). Software/Copyright Disclosure - Tech ID: 2023-003.

Software/Package (selected)
1. Pally, R.*, and Samadi, S. 2022. A Python tool for Flood Image Classification and Semantic Segmentation (funded by NSF). Released strictly using the MIT license.
2. Donratanapat, N.*, Samadi S. 2020. A Google Module for Computing Rapid Surface Runoff (funded by NSF). Released strictly using the MIT license.
3. Sadeghi Tabas, S.*, Samadi S. 2020. A Deep Learning Application for Managing Water Resources Systems (funded by NSF; will be Beta-tested in 2021).
4. Donratanapat, N.*, Samadi S., and Vidal, J. 2019. “FAIS”: Flood Analytics Information System (funded by NSF). Released strictly using the MIT license.
5. Sadeghi Tabas, S.*, Samadi S. 2019. A Web GIS Project Screening Tool (PST, ArcGIS API for JavaScript) for Environmental Assessment(funded by SCDOT).
6. Liu H. *, Hitchcock D., and Samadi S. 2018. A tree-based model and spatial information for stochastic analysis of hydrological extremes. Python tool.


Google Scholar