A methodology to select the maximum level of wavelet decomposition to forecast seven days of daily inflows by a hybrid model wavelet-based artificial neural network (WANN) is proposed. The wavelet decomposition was employed to decompose an input time series into approximation and detail components, and the approximations were used as inputs to artificial neural networks (ANN) for WANN hybrid models. In this study, it was used daily inflows from January 1931 to December 2010 to three Brazilian reservoirs with different discharge patterns, and evaluated the accuracy of the WANN models when using seven different mother-wavelets, including Haar, Daubechies, Biorthogonal, Biorthogonal Reverse, Symlet, Coiflet and Discrete Meyer. It was found that the model performance is dependent on the input sets and the selected mother-wavelets. Based on the obtained results, it was observed that the maximum level of decomposition was five, because upper than this level, independently on the inflow magnitude, there is no guarantee that the WANN hybrid models would perform better than the ANN model.

Graphical Abstract

提出了一种基于混合模型小波的人工神经网络（WANN），通过选择小波分解的最大水平来预测7天的每日流量。利用小波分解将输入时间序列分解为近似分量和细节分量，并将近似值用作WANN混合模型的人工神经网络（ANN）的输入。在这项研究中，它使用了1931年1月至2010年12月的每日流入三个不同排放模式的巴西水库，并使用7个不同的子波（包括Haar，Daubechies，Biorthogonal，Biorthogonal Reverse， Symlet，Coiflet和离散Meyer。发现模型性能取决于输入集和所选的子小波。

图形概要