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

Yıl 2012, Cilt: 8 Sayı: 2, 36 - 44, 17.12.2012

Abdurrahim Aydın , Remzi Eker

Öz

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.

Anahtar Kelimeler

Streamflow, Sediment, Prediction, RBF

Günlük Sediment Yükünün Radyal Temelli Fonksiyon Sinir Ağları Kullanılarak Tahmin Edilmesi

Öz

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.

Anahtar Kelimeler

Akarsu Akış, Sediment, Tahmin, RBF

Kaynakça

• Aik, LE and Zainuddin, Z 2008.An Improved Fast Training Algorithm for RBF NetworksUsing Symmetry-Based Fuzzy C-Means Clustering.MATEMATIKA, 24(2), 141–148.
• Bors, A, G 2001. Introduction of the Radial Basis Function (RBF) Networks, Online Symposium for Electronics Engineers, issue 1, vol. 1, DSP Algorithms: Multimedia, pp. 1-7.
• Caroni, E, Singh,V, Pand Ubertini, L 1984. Rainfall-runoff-sediment yield relation by stochastic modeling. Hydrological Sciences Journal, 29:2, 203-218
• Cigizoglu, HK 2002. Suspended Sediment Estimation and Forecasting using Artificial Neural Networks.Turkish J. Eng. Env. Sci.26: 15-25.
• Fahlman, SE 1988.Fast-learning variations on back propagation: an empirical study. In Proceedings of the1988 Connectionist Models Summer School, D.S.
• Fashtali, FJ 2003. Landuse change and Suspended Sediment Yield Analysis Using RS and GIS a case study in Uromieh lake area (Shar-chi Catchment). International Institute For Geo-Information Science and Earth Observation Enschede, The Netherland.
• Haykin, S 2001. Neural Networks: A comprehensive foundation. Macmillan College Publishing.
• Kerr, SJ 1995. Silt, turbidity and suspended sediments in the aquatic environment: an annotated bibliography and literature review. Ontario Ministry of Natural Resources, Southern Region Science & Technology Transfer Unit Technical Report TR-008. 277 pp.
• Kisi, O, Yuksel, I and Dogan E 2008.Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrological Sciences Journal, 53:6, 1270-1285
• Knighton, D1998. Fluvial Forms and Processes: A New Perspective, Arnold, London.
• Kisi, O, Yuksel, I and Dogan E 2008.Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrological Sciences Journal, 53:6, 1270-1285
• Knighton, D1998. Fluvial Forms and Processes: A New Perspective, Arnold, London.
• Mustafa, MR, Isa, KH and Rezaur, RB 2011. A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River A Case Study in Malaysia. World Academy of Science, Engineering and Technology, 81:372-376.
• NeuroSolution User’s Manual: www.neurosolutions.com Accessed: 07.09.2010
• Orr, M 1996.Introduction to Radial Basis Function Networks. Institute for Adaptive and Neural Computation, Edinburgh Univ.
• Pavelsky, TM and Smith, LC 2009. Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada. Water Resources Research, W11417, 45:1-16
• Singh, VP and Krstanovic, PF 1987. A stochastic model for sediment yield using principle of maximum entropy. Water Resources Research, 23:781-793.
• Wang, YM, Traore, S and Kerh, T 2008.Using Artificial Neural Networks for Modeling Suspended Sediment Concentration.10th WSEAS Int. Conf. on Mathematical Methods and Computational Techniques in Eelectrical Engineering (MMACTEE'08), Sofia, Bulgaria.
• Wang, YM, Kerh, T and Traore, S 2009. Neural Networks Approaches for modelling river suspended sediment concentration due to tropical storms. Global Nest Journal, 11: 457-466.
• Wilamowski, BM 2003.“Neural network architectures and learning,” in Proc.ICIT, Maribor, Slovenia, pp. TU1–TU12.
• Zhu, YM, Lu, XX and Zhou, Y 2007.Suspended sediment flux modeling with artificial neural network: Anexample of the Longchuanjiang River in the Upper YangtzeCatchment, China. Geomorphology 84: 111–125.