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

This Article Abstract 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

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

View larger version (37K): [in this window] [in a new window]   Fig. 1. Observed and simulated pressure head at (a) 0.4-m depth, (b) 1.0-m depth, and (c) 2.6-m depth using minimum, median, and maximum values of the saturated hydraulic conductivity, Ks, derived from laboratory analysis of vadose zone samples. A simulation with median Ks and optimized pore-connectivity parameter l values is also shown.

View larger version (117K): [in this window] [in a new window]   Fig. 2. Pareto optimal solutions (solid circles) of the three-dimensional Pareto trade-off space as determined by the (a–c) NSGA-II algorithm, (d–f) MOSCEM-UA, and (g–i) AMALGAM: (a, d, g) F1–F2 plane, (b, e, h) F1–F3 plane, and (c, f, i) F2–F3 plane of the objective space. The compromise solutions are separately indicated in each panel by the + symbol.

View larger version (30K): [in this window] [in a new window]   Fig. 3. Normalized range of Pareto optimal parameter values for the three vadose zone materials in the Spydia HYDRUS-1D model including the compromise solution (solid lines) and the Pareto extremes (dashed and dashed-dotted lines), i.e., the best-fit solutions for each of the individual objectives F1–F3: (a) using the NSGA-II algorithm, (b) using the MOSCEM-UA algorithm, and (c) using AMALGAM.

View larger version (45K): [in this window] [in a new window]   Fig. 4. Water retention and hydraulic conductivity functions for (a–b) the 0.4-m depth, (c–d) the 1.0-m depth, and (e–f) the 2.6-m depth shown for the compromise solutions of NSGA-II, MOSCEM-UA, and AMALGAM as well as for vadose zone cores analyzed in the laboratory.

View larger version (55K): [in this window] [in a new window]   Fig. 5. Observed and simulated pressure head using the compromise solution parameter set and the best-fit parameter sets for the objectives F1, F2, and F3 (Pareto extremes) determined by AMALGAM at: (a) the 0.4-m depth, (b) the 1.0-m depth, and (c) the 2.6-m depth.

View larger version (25K): [in this window] [in a new window]   Fig. 6. Evolution of the best (minimum) root mean square error (RMSE) values as a function of the number of HYDRUS-1D model evaluations with the NSGA-II and MOSCEM-UA algorithms and AMALGAM: (a) compromise solution RMSE0, (b) best fit for 0.4-m depth RMSE1, (c) best fit for 1.0-m depth RMSE2, and (d) best fit for 2.6-m depth RMSE3. Single-objective SCE-UA runs are also separately included.