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Simple, efficient and robust techniques for automatic multi-objective function parameterisation: case studies of local and global optimisation using APSIM

Citation

Harrison, MT and Roggero, PP and Zavattaro, L, Simple, efficient and robust techniques for automatic multi-objective function parameterisation: case studies of local and global optimisation using APSIM, Environmental Modelling and Software, 117 pp. 109-133. ISSN 1364-8152 (2019) [Refereed Article]


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Copyright Statement

2019 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.envsoft.2019.03.010

Abstract

Several techniques for automatic parameterisation are explored using the software PEST. We parameterised the biophysical systems model APSIM with measurements from a maize cropping experiment with the objective of finding algorithms that resulted in the least distance between modelled and measured data (φ) in the shortest possible time. APSIM parameters were optimised using a weighted least-squares approach that minimised the value of φ. Optimisation techniques included the Gauss-Marquardt-Levenberg (GML) algorithm, singular value decomposition (SVD), least squares with QR decomposition (LSQR), Tikhonov regularisation, and covariance matrix adaptation-evolution strategy (CMAES).

In general, CMAES with log transformed APSIM parameters and larger population size resulted in the lowest φ, but this approach required significantly longer to converge compared with other optimisation algorithms. Regularisation treatments with log transformed parameters also resulted in low φ values when combined with SVD or LSQR; LSQR treatments with no regularisation tended to converge earliest.

In addition to an analysis of several PEST algorithms, this study provides a narrative on how methodologies presented here could be generalised and applied to other models.

Item Details

Item Type:Refereed Article
Keywords:CPU time, genetic algorithm, inverse modelling, optimization, parameterization, regularization, APSIM, PEST, minimization, convergence, Tikhonov regularization, Gauss-Marquardt-Levenberg, coveriance matrix adaptation evolution strategies
Research Division:Mathematical Sciences
Research Group:Numerical and Computational Mathematics
Research Field:Optimisation
Objective Division:Plant Production and Plant Primary Products
Objective Group:Summer Grains and Oilseeds
Objective Field:Maize
UTAS Author:Harrison, MT (Dr Matthew Harrison)
ID Code:131511
Year Published:2019
Deposited By:TIA - Research Institute
Deposited On:2019-03-20
Last Modified:2019-04-29
Downloads:3 View Download Statistics

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