Predicting Annual Tigris River Streamflow at Kut Barrage using SAMS Program

Forecasting synthetic hydrologic data is the primary goal of the Stochastic Analysis, Modeling, and Simulation (SAMS) program. This research examined yearly data on the streamflow of the Kut Barrage on the Tigris River spanning 21 years, from 2003 to 2023. The data were converted using the logarithm transformation method, and the skewness and Filliben tests were run to assess the data's normality. To calculate the parameters of the univariate autoregressive moving average (ARMA) model, historical data were examined and ACF and PACF were shown. From these two graphs, it was concluded that the values ​​of the ARMA model orders must be greater than one, and therefore four types of ARMA models were chosen: ARMA (1,1), ARMA (1,2), ARMA (2,1), and ARMA (2,2). The four ARMA models were compared with two criteria corrected Aikaike information criterion (AICC) and the Schwarz information criterion (SIC), which had the lowest values ​​for the ARMA (1,1) forecasting model, which were 15.753 and 14.431, respectively. The generated data's mean value of 377 is quite near to the historical data's mean value of 380.7 the same is true for the covariance and standard deviation, which for the historical and anticipated data are 177.3 and 0.4836, respectively, and 182.3 and 0.4836.The RMSE value which is 0.34 for the historical and forecast data was found to be an acceptable value indicating that the ARMA (1,1) model is a suitable model for generating data, and the generated data for another 21 years after 2023 is highly accurate and reliable.