Akım ve Sediment Yük Öngörümü İçin Doğrusal Genetik Programlamanın Uygulanması

Nehirlerin morfolojisini, ekosistemi ve özellikle taşkın olaylarını etkileyen iki ana değişken askıdaki sediment ve günlük akımlardır. Yapay sinir ağları (YSA), bu değişkenleri modellemek ve tahmin etmek için yakın zamanda yapılmış çalışmalarda başarıyla kullanılmıştır. Bununla birlikte, bunlar kapalı yöntemlerdir ve pratik uygulamalarda kolaylıkla kullanılamazlar. Bu makalede, İran'daki iki nehirde bu değişkenleri tahmin etmek üzere açık modeller geliştirmek için doğrusal genetik programlama (DGP) yaklaşımı önerilmiştir. DGP tarafından geliştirilen açık ilişkiler (tahmin kuralları), fiziksel tutarlılığı açısından kontrol edilebilen denklemler veya program kodları şeklindedir. Sonuçlar, global maksimum ve minimum akımları elde etme noktasında, DGP’nin YSA’ya göre daha başarılı olduğunu gerek kalibrasyon gerekse doğrulama aşamalarında hataların karelerinin ortalamasının karekökünün en düşük, verimlilik katsayısının ise daha yüksek olmasını sağlayarak göstermiştir.

STREAMFLOW AND SEDIMENT LOAD PREDICTION USING LINEAR GENETIC PROGRAMMING

Daily flow and suspended sediment discharge are two major hydrological variables that affectrivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs)have been successfully used to model and predict these variables in recent studies. However, these areimplicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approachhas been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicitrelationships (prediction rules) evolved by LGP take the form of equations or program codes, which canbe checked for its physical consistency. The results showed that the LGP outperforms ANNs to get globalmaximum and minimum discharges providing lowest root mean squared error and higher coefficient ofefficiency both for training and validation periods.

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