Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China Academic Article

abstract

• 2017 by the authors. Monthly streamflow has elements of stochasticity, seasonality, and periodicity. Spectral analysis and time series analysis can, respectively, be employed to characterize the periodical pattern and the stochastic pattern. Both Burg entropy spectral analysis (BESA) and configurational entropy spectral analysis (CESA) combine spectral analysis and time series analysis. This study compared the predictive performances of BESA and CESA for monthly streamflow forecasting in six basins in Northwest China. Four criteria were selected to evaluate the performances of these two entropy spectral analyses: relative error (RE), root mean square error (RMSE), coefficient of determination (R 2 ), and Nash-Sutcliffe efficiency coefficient (NSE). It was found that in Northwest China, both BESA and CESA forecasted monthly streamflow well with strong correlation. The forecast accuracy of BESA is higher than CESA. For the streamflow with weak correlation, the conclusion is the opposite.

authors

• Singh, Vijay

published proceedings

• ENTROPY

author list (cited authors)

• Zhou, Z., Ju, J., Su, X., Singh, V. P., & Zhang, G.

citation count

• 4

complete list of authors

• Zhou, Zhenghong||Ju, Juanli||Su, Xiaoling||Singh, Vijay P||Zhang, Gengxi

publication date

• November 2017

publisher

• MDPI  Publisher

published in

• Entropy: international and interdisciplinary journal of entropy and information studies  Journal

keywords

• Burg Entropy
• Configurational Entropy
• Entropy Spectral Analysis Time Series Analysis
• Monthly Streamflow Forecasting

Digital Object Identifier (DOI)

• 10.3390/e19110597

start page

• 597

end page

• 597

volume

• 19

issue

• 11

URL

• http://dx.doi.org/10.3390/e19110597