Configurational entropy theory for streamflow forecasting
Configurational entropy theory for streamflow forecasting
复制标题
DOI：
10.1016/j.jhydrol.2014.11.065
复制
发表时间：
2015-02-01
影响因子：
6.4
通讯作者：
Singh, Vijay P.
中科院分区：
文献类型：
--
作者：
Cui, Huijuan;Singh, Vijay P.
本研究发展组态熵理论（CET）用于每月流量预测。该理论由三个主要部分组成：(1)谱密度的确定；(2)倒谱分析参数的确定；(3)自相关函数的推广。与Burg熵理论（BET）的对比表明，CET的光谱密度分辨率更高，光谱峰位置更精确。倒谱分析法比自回归（AR）方法中的Levinson算法和BET中的Levinson- burg算法得到更精确的参数。CET测试使用了19个河流流域的月度流量数据，涵盖了广泛的地理特征。测试表明，CET能捕捉到流量的季节性，并能令人满意地预测高流量和低流量。高流量预测结果令人满意，预测1年的决定系数r(2)大于0.92，预测2年的决定系数r(2)大于0.85，预测60个月的决定系数r(2)大于0.80。然而，预测未来一年的低流量r(2)大于0.50。当考虑相对流域面积来分析流量特征和谱型时，发现上游流量的预测精度（r(2) = 0.84）高于下游流量（r(2) = 0.75）。预测值相对于观测值的残差服从正态分布。(C) 2014 Elsevier B.V.版权所有
This study develops configurational entropy theory (CET) for monthly streamflow forecasting. The theory is comprised of three main parts: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. Comparison with the Burg entropy theory (BET) shows that CET yields higher resolution spectral density with more accurate location of spectral peaks. Cepstrum analysis yields more accurate parameters than the Levinson algorithm in the autoregressive (AR) method and the Levinson-Burg algorithm in BET. CET is tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CET captures streamflow seasonality and satisfactorily forecasts both high and low flows. High flows are satisfactorily forecasted with the coefficient of determination (r(2)) higher than 0.92 for one year ahead of time, with r(2) higher than 0.85 for two years ahead of time, and up to 60 months ahead with r(2) higher than 0.80. However, low flows are forecasted with r(2) higher than 0.50 for one year ahead time. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately (r(2) = 0.84) than downstream streamflow (r(2) = 0.75). Residuals of forecasted values relative to observed values are found to follow normal distribution. (C) 2014 Elsevier B.V. All rights reserved.