Long-lead streamflow forecasting using singular spectrum analysis in the Karkheh basin

Document Type : Research Paper


1 Faculty member of Shiraz Uinversity Ph.D Student of Tehran University

2 Faculty member / Tehran University


In the past decade the different methods have been used to analyze and predict the physical variables, one of which is singular spectrum analysis (SSA) statistical methods. SSA is one of the methods, used in modeling various statistical processes and more recently, its use in various engineering disciplines including water resources, in order to eliminate random components in time series has been expanded. The main objective of this study was to forecast streamflow in the Karkheh basin using singular spectrum analysis. The gage stations in the Karkheh basin (five station) were selected for this study. The high flow period for these gage stations were determined. In order to modeling methods, 70% and 30% of data were used for calibration and validation respectively. The singular spectrum analysis method was used for pre-processing of data and elimination of noise in the time series of streamflow. Then, the recursive algorithm of the singular spectrum analysis model was used to develop forecasts models of streamflow in the Karkheh basin gage stations. To evaluate the performance of the model Normalized root mean square error, mean absolute error and correlation coefficient were used. In the validation the highest and lowest value of the NRMSE and MARE statistics were 0.47 and 0.50 for Pol Chehr station. The lowest value of the NRMSE statistic for Pol Dokhtar and Cham Anjir stations was 0.3 and 0.31 respectively and close to each other and the lowest value of the MARE statistic for Cham Anjir and Pol Dokhtar stations was 0.29 and 0.30 respectively and close to each other. Finally, the best and the weakest results in two stages of calibration and validation were for Cham Anjir and Pol Chehr Stations respectively. The results of this research showed that singular spectrum analysis can be used to forecast streamflow with reasonable accuracy


Main Subjects

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