Journal
ADVANCES IN WATER RESOURCES
Volume 114, Issue -, Pages 164-179Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2018.02.007
Keywords
Sequential Monte Carlo; Genetic algorithm; Bayes; Parameter optimization; Hydrolic models; MCMC
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Funding
- National Key R & D Program of China [2016YFC0500203]
- National Natural Science Foundation of China [31370467, 41571016]
- CAS Interdisciplinary Innovation Team
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Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoffhydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study. (C) 2018 Elsevier Ltd. All rights reserved.
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