Multi-objective global optimization for hydrologic models

By Patrice Ogou Yapo, Hoshin V. Gupta, Soroosh Sorooshian. 1998

The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study.

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “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).”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, background): “…hydrologic measures of performance in addition to the typical statistical measures [e.g., Martinec and Rango, 1989; Michaud and Sorooshian, 1994; Yapo et al., 1998; Gupta et al., 1998; van Werkhoven et al., 2009], but in general it is very difficult to infer processlevel behavior from…”
Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space (2009, methodology): “, 2001], the Shuffled Complex Evolution (SCE-UA) algorithm [Duan et al., 1994; Yapo et al., 1998; Vrugt et al., 2003a, 2003b] the Multiple Start (1)Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium.”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, methodology): “Multiobjective optimization frameworks have been used to highlight trade-offs between different objectives, which may be considered as symptoms of model inadequacy and help point toward model improvement [Gupta et al., 1998; Yapo et al., 1998; Fenicia et al., 2007; Vrugt et al., 2008; Kollat et al.,”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…and multiple different regions of attraction has stimulated the development of population based search algorithms that use multiple different points concurrently to locate the global optimum (Wang, 1991; Duan et al., 1992; Yapo et al., 1998; Seibert, 2000; Khu and Mad-sen, 2005; Chu et al., 2010).”
Dynamically dimensioned search algorithm for computationally efficient watershed model calibration (2007, methodology): “Further evidence of the stature of the SCE algorithm is the incorporation of SCE into advanced algorithms for uncertainty analysis [ Vrugt et al. , 2003a, 2003b] and multiobjective optimization [ Vrugt et al. , 2003a; Yapo et al. , 1998].”
Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. (2007, methodology): “Various calibration algorithms and procedures have been presented in the literature extensively (Rosenbrock, 1960; Nelder and Mead 1965; Duan et al., 1992; Gan and Biftu, 1996; Yapo et al., 1998; Vrugt et al. 2003a and b; see a review in Gupta et al., 2003).”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “For instance, it has been shown that models calibrated using ordinary least-square objective function perform well in predicting extreme conditions, whereas models calibrated with heteroscedastic maximum likelihood estimator perform well in predicting normal flow conditions [Yapo et al., 1998].”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “Traditionally, parameter estimation has been conducted by using deterministic (manual or automatic) calibration techniques that tend to ignore model structural errors and measurement errors [e.g., Duan et al., 1992; Yapo et al., 1998].”
Dalton Medal Lecture: How far can we go in distributed hydrological modelling? (2001, background): “These will be summarised here as the problems of nonlinearity; of scale; of uniqueness; of equifinality; and of uncertainty.”
Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops (2006, background): “Yapo et al. (1998) ; Gupta et al. (1999) have extended Duan’s approach in the context of multi-objective theory.”
Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure (2004, methodology): “…was modeled using the programs MACRO (Jarvis, 1994), HYDRUS5 (Vogel et al., 1996), a neural network ap-tions (e.g., Beven and Binley, 1992; Duan et al., 1992, 2003; Yapo et al., 1998; Gupta et al., 1998). proach (Schaap and Bouten, 1996), and a linear storage model adapted from Huwe et al. (1994).”
Balance between calibration objectives in a conceptual hydrological model (2010, methodology): “in the case of reservoir operation), the calibration problem should be considered in a multi-objective framework (Yapo et al , 1998 ; Madsen, 2000 ; see Efstratiadis & Koutsoyiannis, 2010 , for an excellent review of this topic).”