Türkiye'nin nehirlerinde eksik akım verilerinin tamamlanması için çeşitli veri odaklı tekniklerin performans değerlendirmesi

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 (LR), Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), Multivariate Adaptive regression splines (MARS), and K-nearest neighbor (KNN) methods. Performances of infilling methods were evaluated based on four performance criteria; namely, root mean squared error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and the Kling–Gupta efficiency (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.