Saman Razavi
TitleCited byYear
Review of surrogate modeling in water resources
S Razavi, BA Tolson, DH Burn
Water Resources Research 48 (7), 2012
What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in E arth and E nvironmental systems models
S Razavi, HV Gupta
Water Resources Research 51 (5), 3070-3092, 2015
Numerical assessment of metamodelling strategies in computationally intensive optimization
S Razavi, BA Tolson, DH Burn
Environmental Modelling & Software 34, 67-86, 2012
A new formulation for feedforward neural networks
S Razavi, BA Tolson
IEEE Transactions on neural networks 22 (10), 1588-1598, 2011
A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory
S Razavi, HV Gupta
Water Resources Research 52 (1), 423-439, 2016
A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application
S Razavi, H Gupta
Water Resources Research 52 (1), 440–455, 2016
Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models
R Sheikholeslami, S Razavi
Environmental Modelling & Software 93, 109-126, 2017
Reducing the computational cost of automatic calibration through model preemption
S Razavi, BA Tolson, LS Matott, NR Thomson, A MacLean, FR Seglenieks
Water Resources Research 46 (11), 2010
Evaluation of integrated multisatellite retrievals for GPM (IMERG) over southern Canada against ground precipitation observations: A preliminary assessment
ZE Asong, S Razavi, HS Wheater, JS Wong
Journal of hydrometeorology 18 (4), 1033-1050, 2017
Toward understanding nonstationarity in climate and hydrology through tree ring proxy records
S Razavi, A Elshorbagy, H Wheater, D Sauchyn
Water Resources Research 51 (3), 1813-1830, 2015
Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study
M Karamouz, S Razavi, S Araghinejad
Hydrological Processes: An International Journal 22 (2), 229-241, 2008
An efficient framework for hydrologic model calibration on long data periods
S Razavi, BA Tolson
Water Resources Research 49 (12), 8418-8431, 2013
Reservoir inflow modeling using temporal neural networks with forgetting factor approach
S Razavi, S Araghinejad
Water resources management 23 (1), 39-55, 2009
Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada
JS Wong, S Razavi, BR Bonsal, HS Wheater, ZE Asong
Hydrology and Earth System Sciences 21 (4), 2163-2185, 2017
Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach
F Yassin, S Razavi, H Wheater, G Sapriza‐Azuri, B Davison, A Pietroniro
Hydrological Processes 31 (19), 3320-3333, 2017
Pre-emption strategies for efficient multi-objective optimization: Application to the development of Lake Superior regulation plan
M Asadzadeh, S Razavi, BA Tolson, D Fay
Environmental modelling & software 54, 128-141, 2014
Historical drought patterns over Canada and their teleconnections with large-scale climate signals.
ZE Asong, HS Wheater, B Bonsal, S Razavi, S Kurkute
Hydrology & Earth System Sciences 22 (6), 2018
Introductory overview: Optimization using evolutionary algorithms and other metaheuristics
HR Maier, S Razavi, Z Kapelan, LS Matott, J Kasprzyk, BA Tolson
Environmental modelling & software 114, 195-213, 2019
Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty
A Haghnegahdar, S Razavi, F Yassin, H Wheater
Hydrological Processes 31 (25), 4462-4476, 2017
Adaptive neural networks for flood routing in river systems
S Razavi, M Karamouz
Water international 32 (3), 360-375, 2007
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