HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Wöhling, T. Articles by Barkle, G. F. Search for Related Content PubMed Articles by Wöhling, T. Articles by Barkle, G. F. GeoRef GeoRef Citation Agricola Articles by Wöhling, T. Articles by Barkle, G. F. Related Collections Inverse Procedures/Parameter Estimation Water Flow Models Volcanic Soils

SOIL PHYSICS

Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties

^a Lincoln Environmental Research, Lincoln Ventures Ltd., Ruakura Research Centre, Hamilton, New Zealand
^b Center for Nonlinear Studies (CNLS), Los Alamos National Lab., Los Alamos, NM 87545
^c Aqualinc Research Ltd., P.O. Box 14-041, Enderley, Hamilton, New Zealand

^* Corresponding author (woehling{at}lvlham.lincoln.ac.nz).

Inverse modeling has become increasingly popular for estimating effective hydraulic properties across a range of spatial scales. In recent years, many different algorithms have been developed to solve complex multiobjective optimization problems. In this study, we compared the efficiency of the Nondominated Sorting Genetic Algorithm (NSGA-II), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and AMALGAM, a multialgorithm genetically adaptive search method for multiobjective estimation of soil hydraulic parameters. In our analyses, we implemented the HYDRUS-1D model and used observed pressure head data at three different depths from the Spydia experimental field site in New Zealand. Our optimization problem was posed in a multiobjective context by simultaneously using three complementary RMSE criteria at each depth. We analyzed the trade-off between these criteria and the adherent Pareto uncertainty. The results demonstrate that all three algorithms were able to find a good approximation of the Pareto set of solutions, but differed in the rate of convergence to this distribution. Small differences in performance of the various algorithms were observed because of the relative high dimension of the optimization problem in combination with the presence of multiple local optimal solutions within the three-objective search space. The Pareto parameter sets yielded satisfactory results when simulating the transient tensiometric pressure at predetermined observation points in the investigated vadose zone profile. The overall best parameter set was found by AMALGAM with RMSE values of 0.14, 0.11, and 0.17 m at the 0.4-, 1.0-, and 2.6-m depths, respectively. In contrast, the fit errors were substantially higher at these respective depths, with RMSE values ranging from 0.87 to 1.49 m, when using soil hydraulic parameters derived from laboratory analysis of small vadose zone cores.

Abbreviations: AMALGAM, a multialgorithm genetically adaptive search method • MOSCEM-UA, Multiobjective Shuffled Complex Evolution Metropolis algorithm • MVG, Mualem–van Genuchten • NSGA-II, Nondominated Sorting Genetic Algorithm • OI, Oruanui ignimbrite • P1 and P2, Palaeosols • SCE-UA, Shuffled Complex Evolution algorithm • TI, Taupo ignimbrite

This article has been cited by other articles:

				J. A. Vrugt, P. H. Stauffer, Th. Wohling, B. A. Robinson, and V. V. Vesselinov Inverse Modeling of Subsurface Flow and Transport Properties: A Review with New Developments Vadose Zone J., May 27, 2008; 7(2): 843 - 864. [Abstract] [Full Text] [PDF]