The combined use of wavelet transform and black box models in reservoir inflow modeling

Abstract

In the study presented, different hybrid model approaches are proposed for reservoir inflow modeling from the meteorological data (monthly precipitation, one-month-ahead precipitation and monthly mean temperature data) by the combined use of discrete wavelet transform (DWT) and different black box techniques. Multiple linear regression (MLR), feed forward neural networks (FFNN) and least square support vector machines (LSSVM) were considered as the black box methods. In the modeling strategy, meteorological input data were decomposed into wavelet sub-time series at three resolution levels and ineffective sub-time series were eliminated by Mallows’ Cp based all possible regression method. As a result of all possible regression analyses, 2-months mode of time series of monthly temperature (D1_Tt), 8-months mode of time series (D3_Tt) of monthly temperature and approximation mode of time series (A3_Tt) of monthly temperature were eliminated. Remained effective sub-time series were used as the inputs of MLR, FFNN and LSSVM. When the performances of the training and testing periods were compared, it was observed that the DWTFFNN conjunction model has better results in terms of mean square errors (MSE) and determination coefficients (R2) statistics. The discrete wavelet transform approach also increased the accuracy of multiple linear regression and least squares support vector machines.

Keywords

• hybrid model
• discrete wavelet transform
• mallows’ cp method
• multiple linear regression
• feed forward neural networks
• least squares support vector machines
• reservoir inflow modeling