Wavelet neural network model for reservoir inflow prediction
Wavelet neural network model for reservoir inflow prediction
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DOI：
10.1016/j.scient.2012.10.009
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发表时间：
2012-12
期刊：
影响因子：
1.4
通讯作者：
U. Okkan
中科院分区：
文献类型：
--
作者：
U. Okkan
在本研究中，结合离散小波变换（DWT）和基于 Levenberg-Marquardt 优化算法的前馈神经网络（FFNN），提出了一种用于每月水库流入量预测的小波神经网络（WNN）模型。研究区域覆盖位于土耳其爱琴海地区的凯梅尔大坝盆地。月度气象数据通过DWT分解为小波子时间序列。通过使用所有可能的回归方法并评估 Mallows Cp 系数以防止共线性，消除了无效的子时间序列。然后，有效的子时间序列分量被用作神经网络的新输入。研究中 DWT 还与多元线性回归 (WREG) 相结合。小波神经网络（WNN）模型和WREG的结果与传统的前馈神经网络（FFNN）和多元线性回归（REG）模型进行了比较。当检查基于统计的标准时，我们发现 DWT 方法提高了前馈神经网络和回归方法的性能。研究确定的结果表明，WNN 是对大坝月流入量序列进行建模的成功工具，并且比其他方法能够提供良好的预测性能。
In this study, a Wavelet Neural Network (WNN) model is proposed for monthly reservoir inflow prediction by combining the Discrete Wavelet Transform (DWT) and Levenberg-Marquardt optimization algorithm-based Feed Forward Neural Networks (FFNN). The study area covers the basin of Kemer Dam which is located in the Aegean region of Turkey. Monthly meteorological data were decomposed into wavelet sub-time series by DWT. Ineffective sub-time series have been eliminated by using all possible regression method and evaluating the Mallows’ Cp coefficients to prevent collinearity. Then, effective sub-time series components have been used as the new inputs of neural networks. DWT has been also integrated with multiple linear regressions (WREG) within the study. The results of Wavelet Neural Network (WNN) model and WREG have been compared with conventional Feed Forward Neural Networks (FFNN) and multiple linear regression (REG) models. When the statistical-based criteria are examined, it has been observed that the DWT method has increased the performances of feed forward neural networks and regression methods. The results determined in the study indicate that the WNN is a successful tool to model the monthly inflow series of dam and can give good prediction performances than other methods.