Automatic calibration of a rainfall–runoff model using a fast and elitist multi-objective particle swarm algorithm

In order to successfully calibrate a numerical model, multiple criteria should be considered. Multi-objective genetic algorithms (MOGAs) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. In...

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Veröffentlicht in:Expert systems with applications Jg. 36; H. 5; S. 9533 - 9538
1. Verfasser: Liu, Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2009
Schlagworte:
Calibration
Multiple objectives
Optimisation
Parameter estimation
Rainfall–runoff models
Multiple objectives
Parameter estimation
Rainfall–runoff models
Calibration
Optimisation
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung:In order to successfully calibrate a numerical model, multiple criteria should be considered. Multi-objective genetic algorithms (MOGAs) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. In this paper, a new non-dominated sorting particle swarm optimisation (NSPSO), is proposed, that combines the operations (fast ranking of non-dominated solutions, crowding distance ranking and elitist strategy of combining parent population and offspring population together) of a known MOGA NSGA-II and the other advanced operations (selection and mutation operations) with a single particle swarm optimisation (PSO). The efficacy of this algorithm is demonstrated on the calibration of a rainfall–runoff model, and the comparison is made with the NSGA-II. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well diversity compared to the NSGA-II optimisation framework.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2008.10.086