中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Minimum relative entropy theory for streamflow forecasting with frequency as a random variable

文献类型:SCI/SSCI论文

作者Cui H. J.; Singh, V. P.
发表日期2016
关键词Minimum relative entropy Streamflow forecasting Spectral density Cepstrum analysis Configurational entropy Burg entropy spectral-analysis maximum-entropy general-method cepstrum
英文摘要This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination (r (2)) of 0.876, which is slightly higher in the Upper Mississippi (r (2) = 0.932) than in the Lower Mississippi (r (2) = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively.
出处Stochastic Environmental Research and Risk Assessment
30
6
1545-1563
语种英语
ISSN号1436-3240
DOI标识10.1007/s00477-016-1281-z
源URL[http://ir.igsnrr.ac.cn/handle/311030/42907]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Cui H. J.,Singh, V. P.. Minimum relative entropy theory for streamflow forecasting with frequency as a random variable. 2016.

入库方式: OAI收割

来源:地理科学与资源研究所

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