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Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models
Author(s) -
Gong Wei,
Duan Qingyun,
Li Jianduo,
Wang Chen,
Di Zhenhua,
Ye Aizhong,
Miao Chiyuan,
Dai Yongjiu
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr018230
Subject(s) - mathematical optimization , computer science , calibration , multi objective optimization , surrogate model , scheme (mathematics) , model parameter , algorithm , mathematics , statistics , mathematical analysis
Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling‐based optimization (MO‐ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO‐ASMO algorithm against NSGA‐II and SUMO with 13 test functions and a land surface model ‐ the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO‐ASMO.