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Application of minimum relative entropy theory for streamflow forecasting
Cui, Huijuan1; Singh, Vijay P.2,3
2017-03-01
Source PublicationSTOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN1436-3240
Volume31Issue:3Pages:587-608
Corresponding AuthorCui, Huijuan(cuihj@igsnrr.ac.cn)
AbstractThis paper develops and applies the minimum relative entropy (MRE) theory with spectral power as a random variable for streamflow forecasting. The MRE theory consists of (1) hypothesizing a prior probability distribution for the random variable, (2) determining the spectral power distribution, (3) extending the autocorrelation function, and (4) doing forecasting. The MRE theory was verified using streamflow data from the Mississippi River watershed. The exponential distribution was chosen as a prior probability in applying the MRE theory by evaluating the historical data of the Mississippi River. If no prior information is given, the MRE theory is equivalent to the Burg entropy (BE) theory. The spectral density obtained by the MRE theory led to higher resolution than did the BE theory. The MRE theory did not miss the largest peak at 1/12th frequency, which is the main periodicity of streamflow of the Mississippi River, but the BE theory sometimes did. The MRE theory was found to be capable of forecasting monthly streamflow with a lead time from 12 to 48 months. The coefficient of determination (r(2)) between observed and forecasted stream flows was 0.912 for Upper Mississippi River and was 0.855 for Lower Mississippi River. Both MRE and BE theories were generally more reliable and had longer forecasting lead times than the autoregressive (AR) method. The forecasting lead time for MRE and BE could be as long as 48-60 months, while it was less than 48 months for the AR method. However, BE was comparable to MRE only when observations fitted the AR process well. The MRE theory provided more reliable forecasts than did the BE theory, and the advantage of using MRE is more significant for downstream flows with irregular flow patterns or where the periodicity information is limited. The reliability of monthly streamflow forecasting was the highest for MRE, followed by BE followed by AR.
KeywordMinimum relative entropy Streamflow forecasting Spectral density Burg entropy Autoregressive (AR)
DOI10.1007/s00477-016-1306
WOS KeywordRAINFALL PROBABILISTIC FORECASTS ; WATER-SUPPLY MANAGEMENT ; HYDROLOGIC TIME-SERIES ; SPECTRAL-ANALYSIS ; WAVELET ANALYSES ; NONPARAMETRIC APPROACH ; UNIVARIATE MODEL ; SHORT-TERM ; PART 2 ; IDENTIFICATION
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
WOS SubjectEngineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS IDWOS:000398003000001
PublisherSPRINGER
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64682
Collection中国科学院地理科学与资源研究所
Corresponding AuthorCui, Huijuan
Affiliation1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China
2.Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA
3.Texas A&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843 USA
Recommended Citation
GB/T 7714
Cui, Huijuan,Singh, Vijay P.. Application of minimum relative entropy theory for streamflow forecasting[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,2017,31(3):587-608.
APA Cui, Huijuan,&Singh, Vijay P..(2017).Application of minimum relative entropy theory for streamflow forecasting.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,31(3),587-608.
MLA Cui, Huijuan,et al."Application of minimum relative entropy theory for streamflow forecasting".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 31.3(2017):587-608.
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