Performance evaluation of various data driven techniques for infilling missing streamflow data across Turkey’s rivers

Authors : Muhammet YILMAZ, Fatih TOSUNOĞLU

Pages : 317-328

View : 9 | Download : 5

Publication Date : 2023-05-15

Article Type : Research Paper

Abstract :Missing data with gaps is always an obstacle to effective planning and management of water resources. Complete and reliable hydrological time series are necessary for the optimal design of water resources. A study was conducted to fill in missing streamflow data of 54 observation stations across Turkey. This process was done with the aid of various statistical estimation methods. Estimations were performed by using Linear regression insert ignore into journalissuearticles values(LR);, Artificial neural network insert ignore into journalissuearticles values(ANN);, adaptive neuro-fuzzy inference system insert ignore into journalissuearticles values(ANFIS);, Support vector machine insert ignore into journalissuearticles values(SVM);, Multivariate Adaptive regression splines insert ignore into journalissuearticles values(MARS);, and K-nearest neighbor insert ignore into journalissuearticles values(KNN); methods. Performances of infilling methods were evaluated based on four performance criteria; namely, root mean squared error insert ignore into journalissuearticles values(RMSE);, coefficient of determination insert ignore into journalissuearticles values(R2);, mean absolute error insert ignore into journalissuearticles values(MAE);, and the Kling–Gupta efficiency insert ignore into journalissuearticles values(KGE); during training and test periods. Reliable and long streamflow data from surrounding stations were selected as input to fill in missing streamflow data for an output station. The results revealed that a single method cannot be specified as the best-fit method for the study area. During the test phase, the R2 ranged from 0.54 to 0.99, and the KGE range was between 0.62 and 0.98. This study showed that especially SVM and MARS methods are suitable for estimating missing streamflow data in Turkey’s rivers. These findings will provide reliable streamflow data that can be used in hydrological modeling and water resources planning and management.
Keywords : akım, destek vektör makineleri, Çok değişkenli uyarlanabilir regresyon eğrileri, Turkiye, Eksik değerler