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

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.

Keywords

• Missing values; streamflow; Support vector machine; Multivariate adaptive regression splines; Turkey

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

Abstract

Eksik veriler, su kaynaklarının etkin bir şekilde planlanması ve yönetilmesinin önünde her zaman bir engel teşkil etmektedir. Su kaynaklarının optimal tasarımı için eksiksiz ve güvenilir hidrolojik zaman serileri gereklidir. Türkiye genelinde 54 gözlem istasyonunun eksik akış verilerinin doldurulması için bir çalışma yapılmıştır. Doğrusal regresyon (LR), yapay sinir ağı (ANN), uyarlanabilir nöro-bulanık çıkarım sistemi (ANFIS), Destek vektör makinesi (SVM), Çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve K-en yakın komşu (KNN) kullanılarak tahminler gerçekleştirilmiştir. Yöntemlerinin performansları dört performans kriterine göre değerlendirilmiştir; bunlar, ortalama kare hata (RMSE), belirleme katsayısı (R2), ortalama mutlak hata (MAE) ve Kling-Gupta verimliliği (KGE) dir. Bir istasyonda eksik akış verilerinin doldurulması için, çevredeki istasyonlardan alınan güvenilir ve uzun akış verileri girdi olarak seçilmiştir. Sonuçlar, tek bir yöntemin çalışma alanı için en uygun yöntem olarak belirlenemeyeceğini ortaya koymuştur. Test aşamasında, R2 0,54 ile 0,99 arasında ve KGE aralığı 0,62 ile 0,98 arasındadır. Bu çalışma, özellikle SVM ve MARS yöntemlerinin Türkiye'deki nehirlerdeki eksik akış verilerinin tahmin edilmesi için uygun olduğunu göstermiştir. Bu bulgular, hidrolojik modelleme ve su kaynakları planlaması ve yönetiminde kullanılabilecek güvenilir akış verileri sağlayacaktır.

Keywords

• akım
• destek vektör makineleri
• Çok değişkenli uyarlanabilir regresyon eğrileri
• Turkiye
• Eksik değerler

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