期刊
WATER RESOURCES RESEARCH
卷 52, 期 3, 页码 1984-2008出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2015WR018230
关键词
adaptive sampling; Gaussian processes regression; multiobjective optimization; surrogate model
资金
- National Basic Research Program of China [2015CB953703]
- Natural Science Foundation of China [51309011, 41375139]
- Strategic Priority Research Program for Space Sciences of the Chinese Academy of Sciences [XDA04061200]
- Special Fund for Meteorological Scientific Research in Public Interest [GYHY201506002, CRA-40]
- Fundamental Research Fund for the Central Universities - Beijing Normal University Research Fund [2013YB47]
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.
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