Effective and efficient global optimization for conceptual rainfall‐runoff models

By Qingyun Duan, Soroosh Sorooshian, Vinod Kumar Gupta. 1992

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “It is also a benchmark problem for evaluation of many parameter estimation methods (Duan et al., 1992, 1994; Yapo et al., 1998; Ajami et al., 2007).”
Estimation of root water uptake parameters by inverse modeling with soil water content data (2003, unknown): “Nonuniqueness leads to several parameter sets describing the data, each yielding equivalent minimum values for the objective function [Gupta and Sorooshian, 1983; Duan et al., 1992].”
Split-parameter structure for the automatic calibration of distributed hydrological models (2007, methodology): “Among them, the Genetic Algorithms (Wang, 1991) and Shuffled Complex Evolution – University of Arizona (SCE-UA, Duan et al., 1992 ) methods stand out, according to Madsen (2000).”
Model identification for hydrological forecasting under uncertainty (2005, background): “The difficulty of the task at hand really only became clear in the early 1990s when Duan et al. (1992) conducted a detailed study of the characteristics of the response surface that any search algorithm has to explore.”
Quantifying hydrological modeling errors through a mixture of normal distributions (2007, methodology): “The covariance matrix is estimated based on these last 250 samples and the scaling factor s is adjusted based on the recommendations of Gelman et al. (1995) who state that for higher dimensional problems (dimensions > 5) the optimal acceptance rate is around 0.”
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling (2007, methodology): “…approach using a method of genetic algorithm optimization, the shuffled complex evolution method for global optimization (SCE-UA), developed by Duan et al. (1992), with the Nash and Sutcliffe efficiency mea- sure (CE) (Nash and Sutcliffe, 1970) and water balance (WB) used as optimization…”
Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model (2005, methodology): “Both studies used a Shuffled Complex Evolution (Duan et al., 1992) search algorithm.”
Determination of vegetation cover fraction by inversion of a four-parameter model based on isoline parametrization (2007, methodology): “Moreover, when using the SCE–UA algorithm, our approach proves its robustness relative to high noise level.”
Validation of a watershed model without calibration (2003, methodology): “When one considers the wide range of watershed models and the heavy emphasis on their calibration [Duan et al., 2003], it is surprising how little attention has been given to the problem of model validation. In three recent reviews of watershed modeling [Singh, 1995; Hornberger and Boyer, 1995; Sing”