Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/113656
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Comparison of Newton-type and SCE optimisation algorithms for the calibration of conceptual hydrological models
Author: Qin, Y.
Kuczera, G.
Kavetski, D.
Citation: Australian Journal of Water Resources, 2016; 20(2):169-176
Publisher: Taylor & Francis
Issue Date: 2016
ISSN: 1324-1583
2204-227X
Statement of
Responsibility: 
Youwei Qin, George Kuczera and Dmitri Kavetski
Abstract: Hydrological model calibration has benefited from improved optimisation algorithms and advances in computing power over the last few decades. Stochastic optimisation methods have received particular attention, the SCE search has emerged as one of the best performing global optimisation algorithms for a single objective function. However, the improved robustness of stochastic optimisation usually comes at a considerable computational cost as the number of calibrated parameters increases. This work revisits the use of modern gradient-based algorithms. We investigate the performance of quasi-Newton and Gauss–Newton algorithms using SIMHYD and FUSE-536 calibrated to three Australian catchments. Analysis of the objective function surfaces detects micro-scale roughness and parameter insensitivity in both SIMHYD and FUSE-536. The SCE search provides the most robust performance for calibrating SIMHYD, but, somewhat surprisingly, struggles in the case of FUSE-536. In terms of computational costs, the Newton-type algorithms require about 20 times fewer objective function evaluations than the SCE search for SIMHYD and 50 times fewer evaluations for FUSE-536. Considering the chance of converging to the global optimum and the computational cost, we suggest that modern Newtontype algorithms may be competitive with, or even outperform, the SCE search. Improvements in the robustness of Newton-type algorithms should further increase their competitiveness.
Keywords: Model calibration; parameter optimisation; Newton-type algorithms; SCE search
Description: Published online: 16 Mar 2017.
Rights: © 2017 Engineers Australia
DOI: 10.1080/13241583.2017.1298180
Published version: http://dx.doi.org/10.1080/13241583.2017.1298180
Appears in Collections:Aurora harvest 3
Civil and Environmental Engineering publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.