A New Algorithm of Bayesian Model Averaging Based on SCE - UA Collection Averaging

  • Liu Jun-Hua
  • Zhang Hong-Qin
  • Zhang Cheng-Ming
  • Zhao Tianyu
  • Ma Jing
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 452)


Bayesian Model Averaging (BMA) is a statistical method used for multi-model ensemble forecast system. Firstly, the likelihood function of BMA is improved by eliminating the explicit constraint, that the sum of weights is 1, and use SCE-UA for the minimization of its, which presents a new method for solving the Bayesian model averaging, that is the BMA-SCE-UA method. With three land surface models of soil moisture simulation test of multiple numerical model. By comparing the common Expectation Maximization (EM) method with the SCE-UA method, the results show that: SCE-UA method can improve the simulation performance of soil moisture in a large extent, and the soil moisture obtained by the BMA collection simulation and observation matches well, no matter from the amplitude variation and seasonal variability, which makes it possible that generating high accuracy data set of soil moisture with the method of BMA-SCE-UA and using multiple land surface models.


Bayesian Model Averaging SCE-UA soil moisture optimization algorithm 


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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Liu Jun-Hua
    • 1
    • 2
  • Zhang Hong-Qin
    • 1
  • Zhang Cheng-Ming
    • 1
  • Zhao Tianyu
    • 3
  • Ma Jing
    • 1
  1. 1.College of Information Science and EngineeringShandong Agricultural UniversityTaianChina
  2. 2.Institute of Agricultural standards and Detecting Technology SAASJinanChina
  3. 3.Shandong South-North Water Diversion Corporation LimitedJinanChina

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