Additive Model for Monthly Reservoir Inflow Forecast

Abstract

AbstractReservoir inflow forecasting plays an essential role in reservoir operation and management. Considering the characteristics of monthly inflow (trend, seasonality, and randomness throughout the hydrologic year), an additive model is proposed to forecast monthly reservoir inflow. Because different features are represented by different frequency bands of the time series, historical time series of the monthly inflow are decomposed by ensemble empirical mode decomposition into several intrinsic mode functions and a residue. According to frequency signatures analyzed by Fourier spectral representation, all intrinsic mode functions and residue are grouped into three terms: trend term, periodic term, and stochastic term. To accommodate the different characteristics of the three terms, an autoregressive model, a least-squares support vector machine, and an adaptive neuro-fuzzy inference system model are adopted for the three subforecasts, respectively. The additive model is subsequently used to integrate t...