文章摘要
李雅晴,谢平,桑燕芳,陈杰,赵羽西,吴林倩.水文序列相依变异识别的RIC定阶准则——以自回归模型为例[J].水利学报,2019,50(6):721-731
水文序列相依变异识别的RIC定阶准则——以自回归模型为例
RIC criterion for identifying dependent variation of hydrological time series: with a case study of autoregressive model
投稿时间:2018-11-29  
DOI:10.13243/j.cnki.slxb.20181069
中文关键词: 相依变异  相关系数  自回归模型  AIC准则  BIC准则  RIC准则
英文关键词: dependent variation  correlation coefficient  autoregressive model  AIC  BIC  RIC
基金项目:国家自然科学基金项目(91547205,91647110,51579181,51779176);湖南省重大水利科技项目(湘水科计【2015】13-21);中国科学院青年创新促进会项目(2017074)
作者单位E-mail
李雅晴 武汉大学 水资源与水电工程科学国家重点实验室, 湖北 武汉 430072  
谢平 武汉大学 水资源与水电工程科学国家重点实验室, 湖北 武汉 430072
国家领土主权与海洋权益协同创新中心, 湖北 武汉 430072 
pxie@whu.edu.cn 
桑燕芳 中国科学院 地理科学与资源研究所 陆地水循环与地表过程重点实验室, 北京 100101  
陈杰 武汉大学 水资源与水电工程科学国家重点实验室, 湖北 武汉 430072  
赵羽西 国家电网公司 西南分部, 四川 成都 610000  
吴林倩 武汉大学 水资源与水电工程科学国家重点实验室, 湖北 武汉 430072  
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中文摘要:
      水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的“相依有变异而残差无变异的最小阶数”的检验标准。
英文摘要:
      The dependence of hydrological processes is one of the main manifestations of hydrological vari-ability. It is a difficult problem to determine the model order reasonably when using autoregressive model to fit it. Based on the analysis of AIC and BIC criteria, this paper proposed an autoregressive model's order determination criterion referred to as RIC,which takes the correlation coefficient between the original series and its dependent components as the fitting index and takes the function formula in the form of information entropy as the measurement index of model uncertainty. Taking AR(1), AR(2), AR(3) and AR(4) mod-els as examples for statistical experiments, and the accuracy of this criterion was compared with other or-der determined criteria in different sequence lengths. The results show that RIC has good adaptability to the above models and the accuracy of order determination is much higher than AIC. For the first three-or-der model, RIC performs better than BIC, but is slightly lower than BIC for the fourth-order model. The advantage of the RIC criterion is that it can simultaneously satisfy the requirements of model ordering, de-pendency degree grading and model checking. Applying it to the analysis of measured hydrological se-quence, the results show that the criterion could accurately identify the order of the autoregressive model and conform to the proposed test standard of"minimum order with variation in dependent parts and no vari-ation in residuals".
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