Saman Razavi

Assistant Professor, School of Environment and Sustainability and Global Institute for Water Security

(306) 966-2923


  • Doctor of Philosophy, Department of Civil and Environmental Engineering, University of Waterloo
  • Master of Science, Department of Civil and Environmental Engineering, Amirkabir University, Iran
  • Bachelor of Science, Department of Civil Engineering, Iran University of Science and Technology, Iran

Research Interests

Hydrologic models development and calibration; environmental and water resources systems planning and management; single- and multi-objective optimization, sensitivity analysis, and uncertainty quantification; climate change and impacts on hydrology and water resources; reconstruction of paleo-hydrology – implications for climate change analysis; surrogate modeling, artificial intelligence, and machine learning


Recent Awards

  • Outstanding Reviewer Award, Environmental Modelling & Software, 2014
  • Editors' Choice Award, Water Resources Research, 2013

Select Publications 

  • Asadzadeh M., S. Razavi, B. A. Tolson, D. Fay, and Y. Fan (2014), Pre-emption Strategies for Efficient Multi-objective Optimization: Application to the development of Lake Superior Regulation Plan, Environmental Modelling and Software.
  • Razavi, S., and B. A. Tolson (2013), An efficient framework for hydrologic model calibration on long data periods, Water Resources Research, 49, doi:10.1002/2012WR013442.
  • Razavi, S., M. Asadzadeh, B. A. Tolson, D. Fay, S. Moin, J. Bruxer, Y. Fan (2013), Evaluation of new control structures for regulating the Great Lakes system: a multi-scenario, multi-reservoir optimization approach, Journal of Water Resources Planning Management, 10.1061/(ASCE)WR.1943-5452.0000375.
  • Razavi, S., B. A. Tolson, and D. H. Burn (2012), Review of surrogate modelling in water resources, Water Resources Research,48, W07401, doi:10.1029/2011WR011527. 32 pages.
  • Razavi, S., B. A. Tolson, and D. H. Burn (2012), Numerical assessment of metamodelling strategies in computationally intensive optimization, Environmental Modelling and Software, 34(0), 67-86.
  • Razavi, S. and B. A. Tolson (2011), A new formulation for feedforward neural networks, IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169.
  • Razavi, S., B. A. Tolson, L. S. Matott, N. R. Thomson, A. MacLean, and F. R. Seglenieks (2010), Reducing the computational cost of automatic calibration through model preemption, Water Resources Research, 46, W11523, DOI:10.1029/2009WR008957. 17 pages.