caa a22 4500 606159444 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1831-1 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1831-1 Importance of hybrid models for forecasting of hydrological variable [Elektronische Daten] [Levent Latifoğlu, Özgür Kişi, Fatma Latifoğlu] In this study, a forecasting model for nonlinear and non-stationary hydrological data based on singular spectrum analysis (SSA) and artificial neural networks (ANN) is presented. The stream flow data were decomposed into its independent components using SSA. These sub-bands representing the trend and oscillatory behavior of hydrological data were forecasted 1month ahead using ANN. The forecasted data were obtained with summation of each forecasted sub-bands. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the performance of the proposed model. According to statistical parameters, the hybrid SSA-ANN model was a very promising approach for forecasting of hydrological data. The statistical performance parameters were obtained as MSE=0.00088, MAE=0.0217 and R=0.986. Also, hydrological data were forecasted using single ANN model for the comparison. Results were compared with the SSA-ANN model and showed that the SSA-ANN model was much more accurate than the ANN model for the prediction of 1month ahead stream flow data. To demonstrate the practical utility of the proposed method, SSA-ANN and ANN models were used from 1 to 6months ahead for forecasting of hydrological data. The Natural Computing Applications Forum, 2015 Singular spectrum analysis nationallicence Artificial neural networks nationallicence Stream flow data nationallicence Latifoğlu Levent Engineering Faculty, Erciyes University, Kayseri, Turkey aut Kişi Özgür Architectural and Engineering Faculty, Canik Basari University, Samsun, Turkey aut Latifoğlu Fatma Engineering Faculty, Erciyes University, Kayseri, Turkey aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1669-1680 0941-0643 26:7<1669 2015 26 521 https://doi.org/10.1007/s00521-015-1831-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1831-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Latifoğlu Levent Engineering Faculty, Erciyes University, Kayseri, Turkey aut NATIONALLICENCE 700 1- Kişi Özgür Architectural and Engineering Faculty, Canik Basari University, Samsun, Turkey aut NATIONALLICENCE 700 1- Latifoğlu Fatma Engineering Faculty, Erciyes University, Kayseri, Turkey aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1669-1680 0941-0643 26:7<1669 2015 26 521