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Shang X.-S.,Anhui Key Laboratory of Water Conservancy and Water Resources | Wang S.-C.,Chinese Ministry of Water Resources | Wang Z.-L.,Anhui Key Laboratory of Water Conservancy and Water Resources | Wang D.,Nanjing University
Shuikexue Jinzhan/Advances in Water Science | Year: 2011

It is a key problem of properly selecting and determining the threshold value for wavelet denoising. In this paper, a new method is proposed to adaptively determine the threshold for wavelet analysis. The method uses the character of sample entropy and combines it with wavelet denoising method. Using the method, the sample entropy values are computed for noise time series with different threshold values. The relationship between the sample entropy values and the corresponding threshold values can thus be obtained. As the result, the threshold value is determined at the point with maximum entropy. The method is tested using the synthetic data and annual runoff series from Yingluoxia on the Heihe River basin and Huayuankou on the Yellow River basin. Results show that the method is able to separate the signal and noise from hydrological time series with a good denoising performance. The resulting signal is able to retain the characteristics of hydrological time series and evaluation indexes. The method also offers a new approach to determine the threshold value for wavelet denoising. Source


Shang X.,Nanjing University | Shang X.,Anhui Key Laboratory of Water Conservancy and Water Resources | Wang D.,Nanjing University
Shuili Fadian Xuebao/Journal of Hydroelectric Engineering | Year: 2015

Consideration of historical flood could improve the accuracy and reliability of flood frequency analysis. In practice, however, some historical floods cannot be quantitated and are difficult to use in a common deterministic methods, thus lowering the reliability of parameter estimation and design flood in the flood frequency analysis. This paper focuses on the coupling of a Bayesian method a maximum likelihood estimator in the frequency analysis using non-quantitative historical floods. This coupling method and its application are demonstrated in a case study of synthetic and measured flood series. Compared to several traditional deterministic method, the coupling method not merely makes it possible to make use of non-quantitative historical floods, but can improve estimation accuracy. © All right reserved. Source


Wang D.,Nanjing University | Singh V.P.,Texas A&M University | Shang X.,Nanjing University | Shang X.,Anhui Key Laboratory of Water Conservancy and Water Resources | And 8 more authors.
Journal of Geophysical Research D: Atmospheres | Year: 2014

De-noising meteorologic and hydrologic time series is important to improve the accuracy and reliability of extraction, analysis, simulation, and forecasting. A hybrid approach, combining sample entropy and wavelet de-noising method, is developed to separate noise from original series and is named as AWDA-SE (adaptive wavelet de-noising approach using sample entropy). The AWDA-SE approach adaptively determines the threshold for wavelet analysis. Two kinds of meteorologic and hydrologic data sets, synthetic data set and 3 representative field measured data sets (one is the annual rainfall data of Jinan station and the other two are annual streamflow series from two typical stations in China, Yingluoxia station on the Heihe River, which is little affected by human activities, and Lijin station on the Yellow River, which is greatly affected by human activities), are used to illustrate the approach. The AWDA-SE approach is compared with three conventional de-noising methods, including fixed-form threshold algorithm, Stein unbiased risk estimation algorithm, and minimax algorithm. Results show that the AWDA-SE approach separates effectively the signal and noise of the data sets and is found to be better than the conventional methods. Measures of assessment standards show that the developed approach can be employed to investigate noisy and short time series and can also be applied to other areas. © 2014. American Geophysical Union. All Rights Reserved. Source


Shang X.,Anhui Key Laboratory of Water Conservancy and Water Resources | Shang X.,Anhui and Huaihe River water resources research institute | Wang Z.,Anhui Key Laboratory of Water Conservancy and Water Resources | Wang Z.,Anhui and Huaihe River water resources research institute | Wang D.,Nanjing University
Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering | Year: 2011

The precision and reliability of parameter estimation are influenced with multiple uncertain factors of hydrologic data in hydrologic frequency analysis. In this paper, Bayesian method and curve fitting method were coupled to analyze uncertainties of parameter estimation, and Markov Chain Monte Carlo(MCMC) simulation method based on adaptive metropolis(AM) algorithm was introduced to calculate the Bayesian equation. Type Pearson III distribution(P-III distribution) was taken an example to discuss and analyze the uncertainties of parameter estimation with several measurement data, meanwhile, the influence of sample size and historical information was quantitatively demonstrated for parameter estimation from the view of uncertainties. It shows that the Bayesian method not only offer parameter estimation but also analyze their uncertainties, and further improve the reliability of hydrologic analysis and calculation. Source

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