A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters. (April 2018)

• Record Type: Journal Article
• Authors: Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli
• Is Part Of: Advances in water resources. Volume 114(2018)
• Page Start: 164
• Page End: 179
• Publication Date: 2018-04
• Subjects: Sequential Monte Carlo -- Genetic algorithm -- Bayes -- Parameter optimization -- Hydrolic models -- MCMC; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2018.02.007
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: A new SMC sampler incorporating the genetic, evaluation and Metropolis–Hasting algorithms into the framework of SMC was developed. The new SMC sampler is well suited to handle unknown static parameters of hydrologic models. The new SMC sampler has good abilities in mixing and maintaining particle diversity. Abstract: 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–runoff hydrologic 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.