Prediction of River Discharge using Periodic Autoregressive Model with Constant and Variable Coefficients using Parametric and Nonparametric Methods (Case Study: Houro River, Kakkarza Station).

Time series analysis is divided into two general categories, parametric and nonparametric methods. In this study, the monthly rainfall data in a 35-year statistical period of kakarreza hydrometric station (located in Khorramabad city) was tested by the parametric and nonparametric method for periodic Autoregressive (AR) with constant and variable coefficients individually. Also, winter method and decomposition were tested and evaluated. Regarding the fact that the mean and standard deviations have not the significant difference in parametric and non-parametric models. The results of modeling are almost identical in two situations and in a constant coefficient situation, the autoregressive model with order 2 gives a slightly better result (R2=0.534, ACI=-446 and RMSE=6.07). Generally, periodic autoregressive model with variable coefficients has better performance than autoregressive model with constant coefficients. Analysis of the seasonal decomposition model shows that the residual series are non-random. Also in the winter’s model, the graph of residuals versus fitted values exhibits a specific structure and the graph has a trend, so it cannot be assumed that the residual variance is a constant. As a result, these models do not have the capability to estimate river flow.