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Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Ç oruh basin, T urkey
Author(s) -
Can İbrahim,
Tosunoğlu Fatih,
Kahya Ercan
Publication year - 2012
Publication title -
water and environment journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 37
eISSN - 1747-6593
pISSN - 1747-6585
DOI - 10.1111/j.1747-6593.2012.00337.x
Subject(s) - streamflow , autoregressive model , autoregressive–moving average model , artificial neural network , flood forecasting , time series , series (stratigraphy) , environmental science , structural basin , moving average , hydrology (agriculture) , drainage basin , meteorology , computer science , statistics , mathematics , geography , artificial intelligence , geology , cartography , geotechnical engineering , paleontology
Streamflow modelling is a quite important issue for water resources system planning and management projects, such as dam construction, reservoir operation and flood control. This study demonstrates the application of artificial neural networks ( ANN ) and autoregressive moving average ( ARMA ) models for modelling daily streamflow in Ç oruh basin, T urkey, where there are numerous highly critical power plants either under construction or being projected. Daily streamflow records from nine gauging stations located in the basin were used in this study. In the first phase of our study, ANN and ARMA models were obtained using daily streamflow. In the second phase, 100 synthetic streamflow series were generated using previously determined ANN and ARMA models in order to ensure the preservation of main statistical characteristics of the historical time series. The results have showed that the historical time series have similar statistical parameters to those of the generated time series at 95% confidence level.