Günlük Sediment Yükünün Radyal Temelli Fonksiyon Sinir Ağları Kullanılarak Tahmin Edilmesi

Bu çalışmada, Yapay Sinir Ağları (ANNs) kullanılarak günlük akarsu akış miktarına karşılık gelen günlük askıda sediment miktarları tahmin edilmiştir. Radyal Temelli Fonksiyonlar (RBFs), ANN yöntemi olarak seçilmiş ve linear tangent hyperbolic axon (litanhaxon) ve tangent hyperbolic axon (tanhaxon) transfer fonksiyonları ile Quickprop (QP) ve Delta-bar-Delta (DBD) isimli iki farklı öğrenme algoritması uygulanmıştır. Çoruh Nehri (Türkiye) üzerindeki İspir Ölçüm istasyonunda 1991 ve 1999 yılları arasında ölçülen akarsu akış ve askıda sediment verisi kullanılmıştır. RBF ağ yapısı için öğrenme/kalibrasyon ve test/doğrulama amacıyla toplamda 106 veri kullanılmıştır. Ölçümlerin 76 tanesi (%72) öğrenme için ayrılırken geriye kalanlar test etmek için kullanılmıştır. Geliştirilen tüm RBF ağları bir gizli katman (HL) ve bir proses eleman (PE) veya nörona sahiptir. Ortalama Mutlak Hata (MAE) ve korelasyon katsayısı (R) performans kriteri olarak kullanılmıştır. Geliştirilen ağların MAE performans kriterine göre litanhaxon ile DBD öğrenme algoritması (MAE=0.052) en iyi sonucu verirken sırasıyla litanhaxon ile QP öğrenme algoritması (MAE=0.054), tanhaxon ile DBD öğrenme algoritması (MAE=0.056), tanhaxon ile QP öğrenme algoritması (MAE=0.057) daha iyi sonuç vermiştir. R performans kriterine göre ise tanhaxon ile DBD öğrenme algoritması (R=0.963) en iyi sonucu verirken sırasıyla tanhaxon ile QP öğrenme algoritması (R=0.961), litanhaxon ile QP öğrenme algoritması (R=0.955), litanhaxon ile DBD öğrenme algoritması (R=0.945) daha iyi sonuç vermiştir. Bu çalışma mühendislik uygulamalarında ANN kullanılarak günlük akarsu akış miktarına karşılık gelen askıda sediment miktarının tahmininde RBF ağlarının tatmin edici sonuçlar sağladığını göstermektedir.

Prediction of Daily Suspended Sediment Load Using Radial Basis Function Neural Networks

In this study, daily suspended sediment amount were predicted from corresponding daily streamflow by using Artificial Neural Networks (ANNs). Radial Basis Functions (RBFs) were chosen as ANN method and two different learning algorithms were applied namely Quickprop (QP) and Delta-bar-Delta (DBD) with two different transfer functions called linear tangent hyperbolic axon (litanhaxon) and tangent hyperbolic axon (tanhaxon). Prediction was made by using flow and suspended sediment data of Ispir gauging station on Çoruh River, Turkey between 1991 and 1999. The data, 106 in total, were used as calibration/training and validation/testing sets for the chosen RBF neural network architecture. Of the data obtained 76 measurements (72%) were reserved for the calibration and the remaining data were used for validation. All developed RBF networks have one hidden layer (HL) and one process element (PE) or neuron. Mean Absolute Error (MAE) and coefficient of correlation (R) were used as performance criteria. According to MAE performance criteria of developed networks, DBD learning algorithm with litanhaxon (MAE=0.052) gave best results and following QP learning algorithm with litanhaxon (MAE=0.054), DBD learning algorithm with tanhaxon (MAE=0.056), QP learning algorithm with tanhaxon (MAE=0.057), respectively. According to R performance criteria, DBD learning algorithm with tanhaxon gave best results (R = 0.963) and following QP learning algorithm with tanhaxon (R=0.961), QP learning algorithm with litanhaxon (R=0.955), DBD learning algorithm with litanhaxon (R=0.945), respectively. This study showed that RBF Networks provide satisfactory results in engineering applications for prediction of suspended sediment amount from corresponding daily streamflow by using ANN.

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Kaynak Göster

APA Aydın, A , Eker, R . (2012). Günlük Sediment Yükünün Radyal Temelli Fonksiyon Sinir Ağları Kullanılarak Tahmin Edilmesi . Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi , 8 (2) , 36-44 . Retrieved from https://dergipark.org.tr/tr/pub/duzceod/issue/4821/290812