A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters
文献类型：期刊论文
| 作者 | Zhu, Gaofeng1; Li, Xin2,3; Ma, Jinzhu1; Wang, Yunquan4; Liu, Shaomin5; Huang, Chunlin2; Zhang, Kun1; Hu, Xiaoli2 |
| 刊名 | ADVANCES IN WATER RESOURCES
 |
| 出版日期 | 2018-04-01 |
| 卷号 | 114页码:164-179 |
| ISSN号 | 0309-1708 |
| 关键词 | Sequential Monte Carlo Genetic algorithm Bayes Parameter optimization Hydrolic models MCMC |
| DOI | 10.1016/j.advwatres.2018.02.007 |
| 通讯作者 | Zhu, Gaofeng(zhugf@lzu.edu.cn) |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| WOS关键词 | DATA ASSIMILATION ; DIFFERENTIAL EVOLUTION ; PARTICLE FILTER ; UNCERTAINTY ASSESSMENT ; GLOBAL OPTIMIZATION ; SIMULATION ; ALGORITHM ; SYSTEMS ; SPACES ; ERROR |
| WOS研究方向 | Water Resources |
| WOS类目 | Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000427410700011 |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2558123 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Zhu, Gaofeng |
| 作者单位 | 1.Lanzhou Univ, Minist Educ, Key Lab Western Chinas Environm Syst, Lanzhou 730000, Gansu, Peoples R China 2.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China 4.China Univ Geosci, Sch Environm Studies, Wuhan 430074, Hubei, Peoples R China 5.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Gaofeng,Li, Xin,Ma, Jinzhu,et al. A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters[J]. ADVANCES IN WATER RESOURCES,2018,114:164-179. |
| APA | Zhu, Gaofeng.,Li, Xin.,Ma, Jinzhu.,Wang, Yunquan.,Liu, Shaomin.,...&Hu, Xiaoli.(2018).A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters.ADVANCES IN WATER RESOURCES,114,164-179. |
| MLA | Zhu, Gaofeng,et al."A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters".ADVANCES IN WATER RESOURCES 114(2018):164-179. |
入库方式： iSwitch采集
来源：寒区旱区环境与工程研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。