Stochastic Modeling of Suspended Sediment from Yesilirmak Basin, Turkey

Abstract

This paper presents a methodology on modeling of monthly-suspended sediment data from the Yesilirmak Basin, Turkey. For this purpose, linear stochastic models known as either Box-Jenkins or ARIMA (autoregressive-moving average) were used to simulate monthly sediment data. Diagnostic checks were done for all the models selected by considering the autocorrelation function (ACF) and partial autocorrelation function (PACF). The models that have the minimum Schwarz Bayesian Criterion (SBC) and fulfilled all the diagnostic checks were assumed to best represent the monthly sediment data for sediment-sampling stations in the
Yesilirmak Basin. Modeled data were compared to observed data for a 10-year period, confirming that the generated data preserve the basic statistical properties of the original series.