AllSince 2010
Citations:567547
h-index:1111
i10-index:1312



Patents

[P1] Razavi, S. and Gupta, H.V., Methods and systems for determining global sensitivity of a process, US Patent Application, Filed in 2015 .

Journal Papers

[J23] Haghnegahdar A., Razavi S., Yassin F., Wheater H., (2017), Multi-criteria sensitivity analysis as a diagnostic tool for understanding model behavior and characterizing model uncertainty, Hydrological Processes, doi:10.1002/hyp.11358 .

[J22] Sheikholeslami, R., Yassin, F., Lindenschmidt, K., and Razavi, S., (2017), Improved Understanding of River Ice Processes Using Global Sensitivity Analysis Approaches, ASCE Journal of Hydrologic Engineering, 22(11), doi:10.1061/(ASCE)HE.1943-5584.0001574 .

[J21] Yassin, F., Razavi, S., Wheater, H., Sapriza-Azuri, G., Davison, B., Pietroniro, A., (2017), Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach, Hydrological Processes 31 (19): 3320–3333, doi: 10.1002/hyp.11267 .

[J20] Haghnegahdar, A., and Razavi, S., (2017), Insights into sensitivity analysis of Earth and environmental systems models: On the impact of parameter perturbation scale, Environmental Modelling & Software, 95: 115–131, doi: 10.1016/j.envsoft.2017.03.031 .

[J19] Wong, J.S., Razavi, S., Bonsal, B.R., Wheater, H.S., and Asong, Z.E., (2017), Inter-comparison of Daily Precipitation Products for Large-scale Hydro-climatic Applications Over Canada, Hydrology and Earth System Sciences, 21(4), Pages 2163-2185, doi:10.5194/hess-21-2163-2017 .

[J18] Sheikholeslami, R., and Razavi, S., (2017), Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models, Environmental Modelling & Software, 93: 109–126, doi: 10.1016/j.envsoft.2017.03.010 .

[J17] Asong, Z.E., Razavi, S., Wheater, H.S., and Wong, J.S., (2017), Evaluation of Integrated Multi-Satellite Retrievals for GPM (IMERG) over Southern Canada against Ground Precipitation Observations: A Preliminary Assessment, Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0187.1 .

[J16] Elshorbagy, A., Wagener, T., Razavi, S., and Sauchyn, D., (2016), Correlation and causation in tree-ring-based reconstruction of paleohydrology in cold semiarid regions, Water Resources Research, 52, doi:10.1002/2016WR018985 .

[J15] Razavi, S., and Gupta, H. V., (2016), A new framework for comprehensive, robust, and efficient global sensitivity analysis: II. Application, Water Resources Research, 51, doi:10.1002/2015WR017559 .

[J14] Razavi, S., and Gupta, H. V., (2016), A new framework for comprehensive, robust, and efficient global sensitivity analysis: I. Theory, Water Resources Research, 51, doi:10.1002/2015WR017558 .

[J13] Razavi, S., Elshorbagy, A., Wheater, H. and Sauchyn, D., (2016), Time scale effect and uncertainty in reconstruction of Paleo‐hydrology, Hydrological Processes, doi: 10.1002/hyp.10754 .

[J12] Razavi, S., and H. V. Gupta, (2015), What do we mean by sensitivity analysis? The need for comprehensive characterization of ‘‘global’’ sensitivity in Earth and Environmental systems models, Water Resour. Res., 51, 3070–3092, doi:10.1002/2014WR016527 .

[J11] Razavi, S., A. Elshorbagy, H. Wheater, and D. Sauchyn, (2015), Toward understanding nonstationarity in climate and hydrology through tree ring proxy records, Water Resour. Res., 51, 1813–1830, doi:10.1002/2014WR015696 .

[J10] Asadzadeh M.,Razavi, S., 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, 54: 128-141, doi: 10.1016/j.envsoft.2014.01.005 .

[J9] Razavi, S., and B. A. Tolson, (2013), An efficient framework for hydrologic model calibration on long data periods, Water Resour. Res. 49, doi:10.1002/2012WR013442 .

[J8] 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, J. Water Resour. Plann. Manage., J. Water Resour. Plann. Manage .

[J7] 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. (Recipient of WRR Editors’ Choice Award - AGU Research Spotlight) .

[J6] 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, .

[J5] 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 .

[J4] 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 .

[J3] Razavi, S. and S. Araghinejad, (2009), Reservoir inflow modeling using temporal neural networks with forgetting factor approach, Water Resources Management, 23:1, 39-55, doi: 10.1007/s11269-008-9263-7 .

[J2] Karamouz, M., Razavi, S., and S. Araghinejad, (2008), Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study, Hydrological Processes, 22: 229–241, doi: 10.1002/hyp.6571 .

[J1] Razavi, S. and M. Karamouz, (2007), Adaptive neural networks for flood routing in river systems, Water International, 32: 3, 360–375, doi: 10.1080/02508060708692216 .

Books

[B2] Gupta, H., and Razavi, S., (2017), Chapter 20 - Challenges and Future Outlook of Sensitivity Analysis, In "Sensitivity Analysis in Earth Observation Modelling", Edited by Petropoulos and Srivastava, Elsevier Pages 397-415, ISBN 9780128030110, doi: 10.1016/B978- 0-12-803011-0.00020-3 .

[B1] Razavi, S., (2013), Efficient Optimization and Calibration of Environmental Models, Scholars’ Press , 289 pages, ISBN-13: 978-3-639-51745-3 .

Conference Presentations/Publications

[C49] Hghnegahdar A.,Rzavi S., Sheikholeslami R., (2017), Dealing with model crashes in global sensitivity analysis, CGU/CSAFM joint scientific meeting, Vancouver, Canada (May 28-31) (Oral Presentation) .

[C48] Haghnegahdar A., Elshamy M., Yassin F., Razavi S., Wheater H., and Pietroniro A., (2017), A comprehensive approach to identify dominant controls of the behavior of a land surface-hydrology model across various hydroclimatic conditions, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 23-28) (Oral Presentation) .

[C47] Haghnegahdar, A. and Razavi S., (2016), A novel and efficient Global Sensitivity Analysis Technique for an Enhanced Multi-criteria Analysis of the Behavior of Complex Environmental Models, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 12-16) (Oral Presentation) .

[C46] Yassin, F. A., Razavi, S., and Wheater, H., (2016), Enhanced identification of hydrologic models using streamflow and satellite water storage data: a multi-objective calibration approach, The 2016 Joint Scientific Congress of the CMOS (Canadian Meteorological and Oceanographic Society) and CGU (Canadian Geophysical Union), Fredericton, New Brunswick (May 29–June 2) (Oral Presentation) .

[C45] Wong, J., Razavi, S., and Wheater, H., (2016), Assessment of model-derived precipitation products over Canada, The 2016 Joint Scientific Congress of the CMOS (Canadian Meteorological and Oceanographic Society) and CGU (Canadian Geophysical Union), Fredericton, New Brunswick (May 29–June 2) (Oral Presentation) .

[C44] Razavi, S., Gupta, H.V., and Haghnegahdar, A., (2016), What Constitutes a “Good” Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost, The 2016 Joint Scientific Congress of CMOS (Canadian Meteorological and Oceanographic Society) and CGU (Canadian Geophysical Union), Fredericton, New Brunswick (May 29–June 2) (Poster Presentation) .

[C43] Haghnegahdar, A., and Razavi, S., (2016), An Efficient Approach to Analyze the Behavior of Hydrological Models Using Global Sensitivity Analysis, The 2016 Joint Scientific Congress of the CMOS (Canadian Meteorological and Oceanographic Society) and CGU (Canadian Geophysical Union), Fredericton, New Brunswick (May 29–June 2) (Oral Presentation) .

[C42] Asong, Z.E., Razavi, S., and Wheater, H., (2016), Validation of Integrated MultisatellitE Retrievals for GPM (IMERG) over Canada against Ground Precipitation Observations, The 69th National Conference of the Canadian Water Resources Association (CWRA), Montreal, Canada (May 25-27) (Oral Presentation) .

[C41] Razavi, S., and Gupta, H.V., (2016), A New Framework for Effective and Efficient Global Sensitivity Analysis of Hydrologic and Environmental Systems Models, American Society of Civil Engineers (ASCE)-Environmental and Water Resources Institute (EWRI)’s World Environmental & Water Resources Congress, West Palm Beach, Florida (May 22-26) (Oral Presentation) .

[C40] Haghnegahdar, A., and Razavi, S., (2016), A Multi-criteria Assessment of Sensitivity in Environmental Models, American Society of Civil Engineers (ASCE)-Environmental and Water Resources Institute (EWRI)’s World Environmental & Water Resources Congress, West Palm Beach, Florida (May 22-26) (Poster Presentation) .

[C39] Razavi, S., and Haghnegahdar, A., (2016), Rigorous Evaluation of a New Framework for Sensitivity and Uncertainty Analysis: Variogram Analysis of Response Surfaces (VARS), American Society of Civil Engineers (ASCE)-Environmental and Water Resources Institute (EWRI)’s World Environmental & Water Resources Congress, West Palm Beach, Florida (May 22-26) (Oral Presentation) .

[C38] Razavi, S., Gupta, H.V., and Haghnegahdar, A., (2016), What Constitutes a “Good” Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 17-22) (Oral Presentation) .

[C37] Haghnegahdar, A., Razavi, S., Wheater, H., and Gupta, H.V., (2016), A multi-model multi-objective study to evaluate the role of metric choice on sensitivity assessment, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 17-22) (Poster Presentation) .

[C36] Gharari, S., Hrachowitz, M., Fenicia, F., Matgen, P., Razavi, S., Savenije, H., Gupta, H., and Wheater, H., (2016), How certain are the process parameterizations in our models?, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 17-22) (Poster Presentation) .

[C35] Haghnegahdar, A., and Razavi, S., (2016), How to assess the Efficiency and “Uncertainty” of Global Sensitivity Analysis?, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 17-22) (Poster Presentation) .

[C34] Elshorbagy, A., Wagener, T., Razavi, S., and Sauchyn, D., (2016), Reconstruction of paleohydrology in semi-arid regions for water resources management: Opportunities and challenges, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 17-22) (Poster Presentation) .

[C33] Tolson, B., ..., Razavi, S., Haghnegahdar, H., ... (11 authors), (2015), Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization, Water Resources American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Invited, Oral Presentation) .

[C32] Hill, M., Jakeman, J., Razavi, S., and Tolson, B., (2015), Beauty and the beast: Some perspectives on efficient model analysis, surrogate models, and the future of modeling, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Invited, Oral Presentation) .

[C31] Yassin, F., Razavi, S., Sapriza, G., and Wheater, H., (2015), Enhanced Identification of hydrologic models using streamflow and satellite water storage data: a multi-objective calibration approach, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Poster Presentation) .

[C30] Haghnegahdar, A., Razavi, S., Wheater, H., and Gupta, H., (2015), Sensitivity Analysis and Insights into Hydrological Processes and Uncertainty at Different Scales, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Poster Presentation) .

[C29] Sheikholeslami, R., and Razavi, S., (2015), On the Impact of Uncertainty in Initial Conditions of Hydrologic Models on Prediction, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Oral Presentation) .

[C28] Razavi, S., and Gupta, H.V., (2015), Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 14-18) (Oral Presentation) .

[C27] Razavi, S., (2015), Sensitivity Analysis and Insights for Model Parametrization, Changing Cold Regions Network’s Watershed Modelling Workshop, Saskatoon, Saskatchewan (Sep. 29) (Oral Presentation) .

[C26] Razavi, S., and Gupta, H.V., (2015), A Critical Look at Sensitivity Analysis of Hydrologic Systems Models, American Geophysical Union-Canadian Geophysical Union (CGU-CGU) Joint Assembly, Montreal, QC (May 3-7) (Oral Presentation) .

[C25] Razavi, S., Elshorbagy, A., Wheater, H., and Sauchyn, D., (2015), Evaluation of Paleo-Hydrologic Extremes and Their Uncertainties, American Geophysical Union- Canadian Geophysical Union (CGU-CGU) Joint Assembly, Montreal, QC (May 3-7) (Poster Presentation) .

[C24] Razavi, S., and Gupta, H.V., (2015), New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 12-17) (Poster Presentation) .

[C23] Yassin, F. A., Wheater, H. S., Razavi, S., Azuri, G. S., Davison, B., and Pietroniro, A., (2015), Comprehensive, Process-based Identification of Hydrologic Models using Satellite and In-situ Water Storage Data: A Multi-objective Calibration Approach, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 12-17) (Poster Presentation) .

[C22] Razavi, S., (2015), Sensitivity analysis for hydrological inferences, Discussion meeting on quantification and reduction of uncertainties in hydrological inferences, invited and sponsored by the Indian Academy of Sciences , Bangalore (Coorg), India (Feb. 26-28) .

[C21] Razavi, S., and Gupta, H.V., (2014), What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models, American Geophysical Union (AGU) Meeting, San Francisco, CA (Dec. 15-19) (Oral Presentation) .

[C20] Razavi, S., A. Elshorbagy, H. Wheater, and D. Sauchyn, (2014), Reconstruction of Paleo-hydrologic Data for Vulnerability Assessment of Water Resources Systems, 11th International Conference on Hydroinformatics, New York, USA (Aug. 17-21) (Oral Presentation) .

[C19] Razavi, S., A. Elshorbagy, H. Wheater, and D. Sauchyn, (2014), On the Reconstruction of Paleo-hydrology: a Foundation for More Reliable Water Resources Management, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 27-May 2) (Poster Presentation) .

[C18] Razavi, S., H. Gupta, (2014), Towards More Efficient and Effective Global Sensitivity Analysis, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 27-May 2) (PICO Presentation) .

[C17] Razavi, S., D. Anderson, P. Martin, G. MacMillan, B. Tolson, C. Gabriel, and B. Zhang, (2012), Efficient Calibration of Computationally Intensive Groundwater Models through Surrogate Modelling with Lower Levels of Fidelity, American Geophysical Union (AGU) Fall Meeting, San Francisco, CA (Dec. 3-7) (Poster Presentation) .

[C16] Razavi, S., M. Asadzadeh, B. A. Tolson, D. Fay, S. Moin, J. Bruxer, and Y. Fan, (2012), Evaluation of New Control Structures in the Great Lakes for Better Managing Water Levels in Future, International Association for Great Lakes Research Conference, Cornwall, ON (May 13-17) (Oral Presentation) .

[C15] Asadzadeh, M., Razavi, S., B. A. Tolson, D. Fay, and W. Werick, (2012), A New Rule Curve Based Regulation Plan for Lake Superior, International Association for Great Lakes Research Conference, Cornwall, ON (May 13-17) (Oral Presentation) .

[C14] Razavi, S., B. A. Tolson, D. A. Burn, and F. Seglenieks, (2012), Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 22-27) (Oral Presentation) .

[C13] Razavi, S. and B. A. Tolson, (2012), Efficient Auto-Calibration of Computationally Intensive Hydrologic Models by Running the Model on Short Data Periods, European Geosciences Union (EGU) General Assembly, Vienna, Austria (April 22-27) (Poster Presentation) .

[C12] Razavi, S., B. A. Tolson, and M. Asadzadeh, (2011), Developing Multi-Lake Regulation Plans for the Great Lakes through Multi-Scenario Optimization, American Geophysical Union (AGU) Fall Meeting, San Francisco, CA (Dec. 5-9) (Poster Presentation) .

[C11] Asadzadeh, M., Razavi, S., and B. A. Tolson, (2011), Multi-Objective Lake Superior Regulation, American Geophysical Union (AGU) Fall Meeting, San Francisco, CA (Dec. 5-9) (Poster Presentation) .

[C10] Razavi, S., B. A. Tolson, and D. H. Burn, (2011), Metamodelling: Help or Hindrance in Environmental and Water Resources Modelling?, ASCE World Environmental & Water Resources Congress, Palm Springs, CA (May 21-26) (Oral Presentation) .

[C9] Razavi, S. and B. A. Tolson, (2010), Model Pre-emption in Computationally Constrained Hydrologic Model Automatic Calibration, Student Conference of Canadian Geophysical Union - Hydrologic Section, University of Guelph, Guelph, ON (Dec. 4) (Oral Presentation) .

[C8] Razavi, S. and B. A. Tolson, (2010), Metamodelling in evolutionary versus non-evolutionary optimization algorithms for hydrologic model automatic calibration, Water 2010: Hydrology, Hydraulics and Water Resources in an Uncertain Environment, Quebec City, Quebec (July 5-7) (Oral Presentation) .

[C7] Razavi, S., B. A. Tolson, L. S. Matott, M. Asadzadeh, and F. Seglenieks, (2009), A simple non-invasive efficiency improvement for hydrologic model calibration, American Geophysical Union (AGU) Fall Meeting, San Francisco, CA. (Dec. 14-18) (Poster Presentation) .

[C6] Razavi, S. and B. A. Tolson, (2009), Application of Dynamically Dimensioned Search (DDS) Algorithm in Neural Network Optimization, 33rd IAHR Congress: Water Engineering for a Sustainable Environment, Vancouver, BC (Aug. 10-14) (Oral Presentation) .

[C5] Matott, L. S., B. A. Tolson, and Razavi, S., (2009), Forecasting subsurface model performance during simulation-based optimization, SIAM Conference on Mathematical & Computational Issues in the Geosciences, Leipzig, Germany (June 15–18) (Oral presentation) .

[C4] Razavi, S., B. A. Tolson, L. S. Matott and A. MacLean, (2009), Forecasting hydrologic model prediction errors during calibration, CGU-AGU Joint Assembly, Toronto, ON (May 24-29) (Poster Presentation) .

[C3] Karamouz, M., Razavi, S., and S. Araghinejad, (2005), Long-Lead Rainfall Forecasting using Dynamic Neural Network: Case Study of Western Part of Iran, 1st Iran-Korea Joint Workshop on Climate Modeling, Mashad, Iran (Oral Presentation) .

[C2] Karamouz, M., Razavi, S., and S. Araghinejad, (2005), Application of Temporal Neural Networks in Long-Lead Rainfall Forecasting, ASCE-EWRI Congress, Anchorage, Alaska (Oral Presentation) .

[C1] Karamouz M., Razavi, S., and S. Araghinejad, (2004), Application of artificial neural networks in flood estimation, 4th International Conference on Decision Making in Urban and Civil Engineering, Porto, Portugal (Oral Presentation) .

Peer-reviewed Technical Reports

[T4] Rodriguez-Prado, A., Moges, M., Razavi, S., and Spence, C., (2016), Northwest Territories Hydrological Modelling, Final report prepared for Northwest Territories Power Corporation (March 26) .

[T3] Tolson, B. A., Razavi, S., and M. Asadzadeh, (2011), Formulation and evaluation of new control structures in the Great Lakes system, Technical Report produced for International Upper Great Lakes Study - International Joint Commission Study, 81 pages (Nov. 9) (Project, principal investigator: Tolson) .

[T2] Tolson B. A., M. Asadzadeh, and Razavi, S., (2011), Chapter 3: Restoration Modelling, In Stakhiv G. and Moin S. (Editors), Options for Restoring Lake Michigan-Huron Water Levels: An Exploratory Analysis, Technical Report produced for International Upper Great Lakes Study (IUGLS), pp. 17-35 (May 26) (Project, principal investigator: Tolson) .

[T1] Thomson, N. R., G. Roos, A. Mathai, Razavi, S., (2009), Effectiveness and performance of the Dipole Flow In Situ Tracer Test, Final Report, Ontario Ministry of the Environment, Best in Science (Project #670 400) .