Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration

By Soon-Thiam Khu, Henrik Madsen. 2005

177 citations recorded by the source corpus.

Subfields: Ocean Engineering, Soil Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/1d0ebe4f2c4e03118eef5ba9e187128c1cd93023

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Citations in the corpus

A critical review of integrated urban water modelling - Urban drainage and beyond (2014, methodology): “Khu and Madsen (2005) and Muschalla (2008) effectively illustrate that integrated model calibration/optimisation procedures are evolving into decisionmaking exercises where most optimal solutions of one objective function are no longer sought, but rather a choice of trade-offs along a Pareto front…”
Balance between calibration objectives in a conceptual hydrological model (2010, methodology): “Yapo et al , 1998 ; Khu & Madsen, 2005 ; Schoups et al , 2005 ; Fenicia et al , 2007a ).”
Which objective function to calibrate rainfall–runoff models for low-flow index simulations? (2017, methodology): “Multiobjective techniques can be implemented in the theoretical framework of multi-objective optimization such as a Pareto front (Khu and Madsen 2005, Fenicia et al. 2007), or more simply by deriving aggregated (combined) objective functions (Madsen 2003, Merz and Blöschl 2004, Oudin et al. 2006,…”
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].”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, background): “Khu & Madsen (2005) proposed an automatic routine, based on multi-objective genetic algorithms and Pareto preference ordering, which enables one to sift through the numerous Pareto optimal solutions and retain a short-list of preferred ones for further investigation; this list contains…”