Open AccessWavelet neural network model for reservoir inflow prediction
Author(s) -
Umut Okkan
Publication year - 2012
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2012.10.009
Subject(s) - inflow , artificial neural network , discrete wavelet transform , wavelet , series (stratigraphy) , feedforward neural network , linear regression , collinearity , computer science , time series , wavelet transform , regression , artificial intelligence , mathematics , statistics , machine learning , geology , meteorology , geography , paleontology
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