Automatic calibration of a conceptual rainfall-runoff model using multiple objectives.

By Henrik Madsen. 2000

829 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/8d3bea25f6981cc1ad3c4bad630f298f0e218a37

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In Global and Planetary Change

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

From R-squared to coefficient of model accuracy for assessing "goodness-of-fits" (2020, methodology): “However for NAM, the largest difference between the IQR of observed and that of modeled series was obtained using R-squared.”
Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008, methodology): “One response to this problem, highlighted and used in this study, is to pose the optimization problem in a multiobjective context (Gupta et al., 1998; Boyle et al., 2000; Madsen, 2000, 2003; Deb et al., 2002; Vrugt et al., 2003a).”
Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, background): “, RMS and MNSD) to improve accuracy of simulating both low flows as well as peak flows (Madsen 2000; Efstratiadis and Koutsoyiannis 2010).”
Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. (2007, methodology): “…may give very similar model performance measured with respect to some likelihood measure (or single objective function), whereas the latter is referred to as multi-objective equivalence of parameter sets and defined in terms of the Pareto criterion (see e.g. Madsen (2000) for a discussion on this).”
Simultaneous heat and water (SHAW) model: model use, calibration, and validation. (2012, background): “, the single parameter set that represents the least amount of tradeoff between j objective functions (Madsen, 2000; Mertens et al., 2006).”
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration (2013, background): “…example through the development of signatures representing different aspects of the data (e.g., Gupta et al., 1998, 2008; Boyle et al., 2000, 2001; Madsen, 2000; Fenicia et al., 2006; Rouhani et al., 2007; Khu et al., 2008; Winsemius et al., 2009; Bulygina and Gupta, 2010; McMillan et al., 2011;…”
Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration (2002, methodology): “In order to calibrate a model, values of the model parameters are selected so that the model stimulates the hydrological behavior of the catchment as closely as possible(Madsen, 2000).”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “It has been used in many different applications and studies [51,33,34,27].”
Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration (2005, methodology): “With the rapid advances in computational power and population-based optimization techniques, researchers began to include up to four or more objective functions in the various automatic calibration techniques [Gupta et al., 1998; Boyle et al., 2000; Madsen, 2000; Madsen et al., 2002].”
Improvement of hydrological model calibration by selecting multiple parameter ranges (2016, positive): “Compared with the trial–error method, it is more efficient and effective, avoiding the interference of anthropogenic factors (Madsen, 2000; Getirana, 2010).”
Balance between calibration objectives in a conceptual hydrological model (2010, methodology): “When multiple purposes are of interest (e.g. 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).”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, methodology): “Further problems arise when the criteria are non-commeasurable, which requires proper scaling to avoid over-emphasis of specific components of the objective function, in contrast to others (Madsen, 2000).”
Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, methodology): “NAM (Nedbor-Afstromnings-Model) [Madsen, 2000] simulates runoff using three storage components: surface storage, root zone storage, and a groundwater store.”
Model identification for hydrological forecasting under uncertainty (2005, positive): “…to assess the level of structural uncertainty present in hydrological models using multi-objective approaches to demonstrate the inability of the model to simultaneously fit all system response modes (Gupta et al. 1998; Boyle et al. 2000; Madsen 2000; Wagener et al. 2001, 2003a; Vrugt et al 2003a).”
Split-parameter structure for the automatic calibration of distributed hydrological models (2007, methodology): “In a rainfall-runoff conceptual model, the reliability of the results depends on the input data quality, the model structure and the calibration process (Sorooshian et al., 1993; Madsen, 2000; Lide´n and Harlin, 2000).”