Optimal use of the SCE-UA global optimization method for calibrating watershed models

By Qingyun Duan, Soroosh Sorooshian, Vinod Kumar Gupta. 1994

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “Computer simulation models generally contain model parameters that cannot be measured directly but can only be inferred by calibration, a trial-and-error process that adjusts the parameter values to fit themodel simulation outputs to the observations of the real system (Duan et al., 1994).”
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. (2000, methodology): “The SCE method has been widely applied for calibration of various conceptual rainfall–runoff models, including the Sacramento model (Sorooshian et al., 1993; Duan et al., 1994; Gan and Biftu, 1996; Yapo et al., 1996; Gan et al., 1997), the Tank model (Tanakamaru and Burges, 1996; Cooper et al.”
Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction (2010, methodology): “[32] In line with Duan et al. [1994] , the SCE optimizer used 10 complexes, with 2Nd + 1 points in each complex, Nd + 1 points in each subcomplex and 2Nd + 1 evolution steps before shuffling (where Nd is the dimensionality of the objective function, here equal to the number of model parameters).”
Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment (2014, methodology): “However, many more parameters must be determined, which would increase the complexity of calculations, and repeated testing is needed before SCE-UA can be used effectively.(18) By comparison, the less complex PSO algorithm can be run with a simple encoded mode and without genetic operators, such as ”
Uncertainty in the environmental modelling process - A framework and guidance (2007, unknown): “This is also denoted as automatic calibration (Duan et al., 1994; Doherty, 2003).”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “First, we calibrate the HyMod model to 6 water years (1961–1966) of Leaf River data using the Shuffled Complex Evolution algorithm (SCE‐UA) [Duan et al., 1994] to find parameters that minimize the mean squared error of Box‐ Cox transformed flow (l = 0.3).”
A spatial regularization approach to parameter estimation for a distributed watershed model (2008, methodology): “…are known to be effective and efficient at finding the global minimum of a parameter estimation problem even for complex watershed models [e.g., Duan et al., 1992, 1993, 1994; Sorooshian et al., 1993; Luce and Cundy, 1994; Gan and Biftu, 1996; Tanakamaru, 1995; Kuczera, 1997; Hogue et al.,…”
Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference (2007, methodology): “In order to avoid problems of finding the maximum of the posterior and long burn-in phases, Markov Chains should be started in the neighbourhood of the maximum of the posterior distribution calculated with a global search algorithm (e.g. Duan et al., 1992, 1993, 1994).”
Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space (2009, methodology): “…(PEST) method [Doherty, 2001], Bayesian recursive parameter estimation [Thiemann et al., 2001], the Shuffled Complex Evolution (SCE-UA) algorithm [Duan et al., 1994; Yapo et al., 1998; Vrugt et al., 2003a, 2003b] the Multiple Start 1Laboratory of Hydrology and Water Management, Ghent University,…”
Assessing the impacts of parameter uncertainty for computationally expensive groundwater models (2006, methodology): “A variety of optimization methods including classical derivative-based methods [Dai and Samper, 2004; Hill et al., 1998; Cheng and Yeh, 1992; Yeh, 1986], genetic algorithms [Giacobbo et al., 2002], clustering and multistart local search methods [Solomatine et al., 1999], shuffled complex evolution a”
Using support vector machines for long-term discharge prediction (2006, methodology): “The SCE-UA technique has been used successfully in the area of surface and subsurface hydrology for the calibration of rainfall–runoff models and identification of parameters of aquifer formation (Duan et al., 1994).”
Effective and efficient algorithm for multiobjective optimization of hydrologic models (2003, methodology): “The MOSCEM-UA algorithm combines the strengths of (1) the complex shuffling employed in the SCE-UA algorithm [Duan et al., 1992, 1993], (2) the probabilistic covariance-annealing search procedure of the SCEM-UA algorithm [Vrugt et al., 2003], and (3) our improved version of the fitness assignment co”