More efficient PEST compatible model independent model calibration |
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| Authors: | Brian E Skahill Jeffrey S Baggett Susan Frankenstein Charles W Downer |
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| Affiliation: | 1. Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, Hydrologic Systems Branch, Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA;2. Department of Mathematics, University of Wisconsin – La Crosse, La Crosse, WI 54601, USA;3. Cold Regions Research and Engineering Laboratory, U.S. Army Engineer Research and Development Center, 72 Lyme Road, Hanover, NH 03755, USA;1. CSIRO Ecosystem Sciences and Sustainable Agriculture Flagship, Waite Campus, Urrbrae, SA 5064, Australia;2. Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany;3. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China;1. Networks Laboratory, Department of Computer Science, University of California, Davis, CA 95616, USA;2. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302, India;1. Department of Computer Science, Khoy Branch, Islamic Azad University, Khoy, Iran;2. School of Information and Communication Technology, Griffith University, Nathan, Brisbane, QLD 4111, Australia;1. Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA;2. Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA |
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| Abstract: | This article describes some of the capabilities encapsulated within the Model Independent Calibration and Uncertainty Analysis Toolbox (MICUT), which was written to support the popular PEST model independent interface. We have implemented a secant version of the Levenberg–Marquardt (LM) method that requires far fewer model calls for local search than the PEST LM methodology. Efficiency studies on three distinct environmental model structures (HSPF, FASST, and GSSHA) show that we can find comparable local minima with 36–84% fewer model calls than a conventional model independent LM application. Using the secant LM method for local search, MICUT also supports global optimization through the use of a slightly modified version of a stochastic global search technique called Multi-Level Single Linkage Rinnooy Kan, A.H.G., Timmer, G., 1987a. Stochastic global optimization methods, part I: clustering methods. Math. Program. 39, 27–56; Rinnooy Kan, A.H.G., Timmer, G., 1987b. Stochastic global optimization methods, part ii: multi level methods. Math. Program. 39, 57–78.]. Comparison studies with three environmental models suggest that the stochastic global optimization algorithm in MICUT is at least as, and sometimes more efficient and reliable than the global optimization algorithms available in PEST. |
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