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基于贝叶斯方法的水位流量关系——以东江干流为例
  • ISSN号：1671-8836
  • 期刊名称：《武汉大学学报：理学版》
  • 时间：0
  • 分类：P333[天文地球—水文科学;水利工程—水文学及水资源;天文地球—地球物理学]
  • 作者机构：[1]中山大学水资源与环境系,广东广州510275, [2]中山大学华南地区水循环与水安全广东省普通高校重点实验室,广东广州510275, [3]中山大学地理科学与规划学院/广东省城市化与地理环境空间模拟重点实验室,广东广州510275
  • 相关基金：国家自然科学基金（41071020）;新世纪优秀人才支持计划资助项目
作者： 顾西辉[1,2,3], 张强[1,2,3], 陈晓宏[1,2,3], 江涛[1,2,3]
关键词： 贝叶斯方法, 马尔科夫链蒙特卡洛(MCMC)算法, 水位流量关系曲线, 置信区间, Bayesian methods Markov chain Monte Carlo（MCMC） algorithm rating curve confidence interval
中文摘要：

以东江干流（珠江流域支流）河源、岭下和博罗3个测站水位-流量数据为例,运用贝叶斯方法拟合水位流量关系曲线中的幂律模型.以东江干流历年实测数据构建合理的先验分布为基础,结合似然函数,导出后验分布,并用马尔科夫链蒙特卡洛（MCMC）算法估计后验分布中的参数.结果表明：贝叶斯方法能够合理推断水位流量关系曲线中的幂律模型并结合MCMC算法进行参数估计,且能够提供拟合的水位流量关系曲线的95%置信区间;相比最大似然估计法,贝叶斯方法在曲线的外延性表现更好.

英文摘要：

This paper presents a Bayesian approach for fitting the power-law rating curve model to a set of stage- discharge measurements of Heyuan, Lingxia and Boluo, respectively, which are located in the East River. After crea- ting a reasonable prior distribution with the previous stage-discharge measurements, the posterior distribution is de- rived with the likelihood function. Then an efficient MCMC algorithm for the parameters estimation is used. The stud- y results show that~ Bayesian approach is reasonable for fitting the power-law rating curve model and estimating the parameters with MCMC algorithm. Also, the 95 % confidence interval is created by Bayesian approach~ Bayesian ap- proach performs better on extension of rating curves than the maximum likelihood estimation.

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期刊信息
  • 《武汉大学学报：理学版》
  • 中国科技核心期刊
  • 主管单位:中华人民共和国2教育部
  • 主办单位:武汉大学
  • 主编：刘经南
  • 地址：湖北武昌珞珈山
  • 邮编：430072
  • 邮箱：whdz@whu.edu.cn
  • 电话：027-68756952
  • 国际标准刊号：ISSN：1671-8836
  • 国内统一刊号：ISSN：42-1674/N
  • 邮发代号:38-8
  • 获奖情况:
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国化学文摘（网络版）,美国数学评论（网络版）,德国数学文摘,荷兰文摘与引文数据库,美国剑桥科学文摘,英国科学文摘数据库,英国动物学记录,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:6988