Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction

Regarding the complexity and limitations of current knowledge, monthly inflow prediction is often not sufficiently accurate and cannot fulfil the needs in water resource planning. Such time series consist of periodic and random components. Thus, by using data pre-processing methods, it is possible to reduce the problematic effects of these components in the modeling process. Monthly inflow methods encompass statistical and soft computing methods. Each of these methods has advantages and disadvantages. In this study, a hybrid model comprising both methods' advantages is presented. This four-step model includes seasonal autoregressive integrated moving average (SARIMA) and adaptive neuro fuzzy inference systems (ANFIS), which is a new hybrid model (SARIMA-ANFIS). The first step entails data pre-processing to prepare the data for linear component modeling. In the second step, the linear and nonlinear terms are estimated by the SARIMA model. In the third step, some goodness of fit tests are applied to investigate the validity of the linear and nonlinear components of decomposed inflows and SARIMA model parameters. Upon the confident correct selection of components, in the fourth step the nonlinear components are modeled by ANFIS. In this method, ANN modeling is used instead of ANFIS (SARIMA-ANN model). The result comparison indicates that the ANFIS is more accurate than artificial neural networks (ANN) and SARIMA-ANN models, and SARIMA-ANFIS is the superior model among all.