Streamflow and Sediment Load Prediction Using Linear Genetic Programming

Yıl 2018, Cilt: 23 Sayı: 2, 323 - 332, 31.08.2018

Ali Danandeh Mehr Ali Unal Şorman

https://doi.org/10.17482/uumfd.352833

Cited By: 1

Öz

Daily flow and suspended sediment
discharge are two major hydrologıcal variables that affect rivers’ 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 are implicit and cannot be simply used in practice. In
this paper, linear genetic programming (LGP) approach has been suggested to
develop explicit models to predict these variables in two rivers in Iran. The
explicit relationships (prediction rules) evolved by LGP take the form of
equations or program codes, which can be checked for its physical consistency.
The results showed that the LGP outperforms ANNs in terms of root mean squared
error and coefficient of efficiency.

Anahtar Kelimeler

Daily discharge, sediment, prediction, linear genetic programming, artificial neural networks

AKIM VE SEDIMENT YÜK ÖNGÖRÜMÜ İÇIN DOĞRUSAL GENETIK PROGRAMLAMANIN UYGULANMASI

Öz

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. 

Anahtar Kelimeler

Günlük akım, Sediment, Öngörüm, Doğrusal Genetik Pogramlama, Yapay sinir ağları

Kaynakça

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