Estimation of Suspended Sediment in Dam-impacted Streams Using Regression-based Methods

Abstract

The estimation of suspended sediment (SS) is a crucial factor for dam reservoirs, river ecosystems, structural changes in water resources, various operational activities, environmental safety, and water management. In this study, classical regression analysis (CRA) and multivariate adaptive regression splines (MARS) methods were used to estimate the mean daily SS (mg/L) of the Patapsco River in the United States. Mean daily discharge (Q), turbidity (T) and SS data from the Catonsville and Elkridge observation stations were used to build the models. For each station, 1199 (65.74%) of the 1824 data recorded between October 2016 and September 2021 were used for training, 300 (16.45%) for validation and 325 (17.81%) for testing datasets. The performance of the developed models was evaluated using the root mean square error, the mean absolute error, and the Nash-Sutcliffe efficiency coefficient statistics. Three different models were developed for estimating SS using Q and T parameters. The model including both Q and T parameters together showed higher estimation performance than the other models at both stations. In addition, the CRA method showed high performance in the training dataset, while the MARS method showed high performance in the validation and testing datasets. It was concluded that the MARS method demonstrated superior estimation performance compared to the CRA method.

Keywords

• Suspended Sediment
• MARS
• Classical Regression Analysis
• Modeling
• Patapsco River Basin

Regresyon Tabanlı Yöntemler Kullanılarak Baraj Etkisindeki Akarsularda Askıda Katı Madde Tahmini

Abstract

Askıda katı madde (AKM) tahmini, baraj gölleri, nehir ekosistemleri, su kaynaklarının yapısal değişiklikleri, çeşitli operasyonel faaliyetler, çevre güvenliği ve su yönetimi bakımından oldukça önemlidir. Bu çalışmada Amerika Birleşik Devletleri’ndeki Patapsco Nehri’nin günlük ortalama AKM (mg/L) değerlerini tahmin etmek için klasik regresyon analizi (KRA) ve çok değişkenli uyarlanabilir regresyon eğrileri (ÇDURE) yöntemleri kullanılmıştır. Modelleri oluşturmak için Catonsville ve Elkridge gözlem istasyonlarından alınan günlük ortalama debi (Q), bulanıklık (T) ve AKM verileri kullanılmıştır. Ekim 2016 ile Eylül 2021 dönemini kapsayan 1824 veriden 1199’u eğitim (%65.74), 300’ü doğrulama (%16.45) ve 325’i test (%17.81) seti için kullanılmıştır. Geliştirilen modellerin performansları ortalama karesel hatanın karekökü, ortalama mutlak hata ve Nash-Sutcliffe verimlilik katsayısı istatistikleri kullanılarak değerlendirilmiştir. AKM tahmini için Q ve T parametreleri ile üç farklı model kurulmuştur. Q ve T parametrelerinin birlikte kullanıldığı model diğer modellere kıyasla her iki istasyonda da daha yüksek tahmin performansı göstermiştir. Ayrıca eğitim veri setinde KRA yöntemi, doğrulama ve test veri setlerinde ise ÇDURE yöntemi yüksek performans göstermiştir. ÇDURE yönteminin KRA yöntemine kıyasla daha yüksek tahmin performansı sergilediği sonucuna ulaşılmıştır.

Keywords

• Askıda Katı Madde
• ÇDURE
• Klasik Regresyon Analizi
• Modelleme
• Patapsco Nehri Havzası

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