Water Resources Management

, Volume 31, Issue 4, pp 1343–1359 | Cite as

An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models

  • Meral Buyukyildiz
  • Serife Yurdagul Kumcu


Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R2).


Suspended sediment load Support vector machine Artificial neural network Adaptive network based fuzzy inference system Coruh River 


  1. Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Wan Mohtar WHM, Hussain A (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245CrossRefGoogle Scholar
  2. Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modeling and Software 22:2–13CrossRefGoogle Scholar
  3. Broomhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Systems 2(6):568–576Google Scholar
  4. Caudill M, Butler C (1992) Understanding neural networks: 1 basic networks. The MIT Press, CambridgeGoogle Scholar
  5. Chong EKP, Zak SH (1996) An introduction to optimization. John Wiley&Sons, IncGoogle Scholar
  6. Cigizoglu HK (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences 48(3):349–361Google Scholar
  7. Cigizoglu HK, Alp M (2005) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37:63–68CrossRefGoogle Scholar
  8. Cigizoglu HK, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317(3–4):221–238CrossRefGoogle Scholar
  9. Dawson CW, Wilby RL (1998) An artificial neural network approach to rainfall –runoff modeling. Hydrol Sci J 43(1):47–66CrossRefGoogle Scholar
  10. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108CrossRefGoogle Scholar
  11. Demirel O, Kakilli A, Tektaş M (2010) ANFIS ve ARMA modelleri ile Elektrik enerjisi yük tahmini. Gazi Üniv Müh Mim Fak Dergisi 25(3):601–610 (in Turkish)Google Scholar
  12. Dogan E (2009) Katı madde konsantrasyonunun yapay sinir ağlarını kullanarak tahmin edilmesi. İMO Teknik Dergi 302:4567–4582 (in Turkish)Google Scholar
  13. Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour Manag 28:4765–4779CrossRefGoogle Scholar
  14. Ekici S (2007) Elektrik Güç Sistemlerinde Akıllı Sistemler Yardımıyla Arıza Tipi ve Yerinin Belirlenmesi. PhD Thesis. Fırat Üniversitesi. Fen Bilimleri Enstitüsü (in Turkish)Google Scholar
  15. Goyal MK (2014) Modeling of sediment yield prediction using M5 model tree algorithm and wavelet regression. Water Resour Manag 28:1991–2003CrossRefGoogle Scholar
  16. Hassan M, Shamim MA, Sikandar A, Mehmood I, Ahmed I, Ashiq S, Khitab A (2015) Development of sediment load estimation models by using artificial neural networking techniques. Environ Monit Assess 187(11):686CrossRefGoogle Scholar
  17. Hoya T, Chambers JA (2001) Heuristic pattern correction scheme using adaptively trained generalized regression neural networks. IEEE Trans. on Neural Networks 12(1):91–100CrossRefGoogle Scholar
  18. Jang JSR (1993) ANFIS adaptive –network-based-fuzzy inference systems. IEEE trans. On. Systems, Man and Cybernetics 23(3):665–685CrossRefGoogle Scholar
  19. Jang JSR, Tsai C, Mizutani E (1997) Neuro- fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc., N.JGoogle Scholar
  20. Kisi O, Ozkan C, Akay B (2012a) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428–429:94–103CrossRefGoogle Scholar
  21. Kisi O, Dailr AH, Cimen E, Shiri J (2012b) Suspended sediment modeling using genetic programming and soft computing tecniques. J Hydrol 450-451:48–58CrossRefGoogle Scholar
  22. Kumar SA, Ojha C, Goyal M, Singh R, Swamee P (2012) Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms. J Hydrol Eng 17(3):394–404CrossRefGoogle Scholar
  23. Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62CrossRefGoogle Scholar
  24. Melesse AM, Ahmad S, McClaina ME, Wang X, Limd YH (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98:855–866CrossRefGoogle Scholar
  25. Miller CB (1951) Analysis of flow–duration, sediment- rating curver method of conputing sediment yield. U.S. Department of Interior, Bureau of Reclamation Sedimantation, Denver, ColoradoGoogle Scholar
  26. Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533CrossRefGoogle Scholar
  27. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantifi cation of accuracy in watershed simulations. Trans ASABE 50:885–900CrossRefGoogle Scholar
  28. Muftuoglu RF (1980) Akarsu Yapıları, Cilt 1. İTÜ İnşaat Fakültesi Matbaası, İstanbul (in Turkish)Google Scholar
  29. Nourani V, Alizadeh F, Roushangar K (2016) Evaluation of a two-stage SVM and spatial statistics methods for modeling monthly river suspended sediment load. Water Resour Manag 30:393–407CrossRefGoogle Scholar
  30. Ozturk F, Apaydin H, Walling DE (2001) Suspended sediment loads through flood events for streams of Sakarya Basin, Turkish J Eng Env. TÜBİTAK 25:643–650Google Scholar
  31. Paredes V, Vidal E (2000) A class-dependent weighted dissimilarity measure for nearest neighbor classification problems. Pattern Recogn Lett 21:1027–1036CrossRefGoogle Scholar
  32. Park J (2006) Uncertainty and sensitivity analysis in support vector machines: Robuts optimization and uncertain programming approaches, dissertation. Norman, OklahomaGoogle Scholar
  33. Partal T, Cigizoglu HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural network. J Hydrol 358:317–331CrossRefGoogle Scholar
  34. Salat R, Osowski S (2004) Accurate fault location in the power transmission line using support vector machine approach. Power Systems IEEE Transactions on 19:879–886CrossRefGoogle Scholar
  35. Sen Z (2004) Yapay Sinir Ağı İlkeleri. İstanbul, Su Vakfı Yayınları (in Turkish)Google Scholar
  36. Shepherd AJ (1997) Second-order methods for neural networks. Springer Verlag London Ltd.Google Scholar
  37. Singh A, Imtiyaz M, Isaac RK, Denis DM (2013) Comparison of artificial neural network models for sediment yield prediction at single gauging station of watershed in eastern India. J Hydrol Eng 18(1):115–120CrossRefGoogle Scholar
  38. Sivakumar B (2006) Suspended sediment load estimation and the problem of inadequate data sampling: a fractal view. Earth Surf Process Landf 31:414–427CrossRefGoogle Scholar
  39. Skapura DM (1996) Building neural networks. Addison-Wesley, New YorkGoogle Scholar
  40. Specht DF (1991) General regression neural network. IEEE Trans on Neural Networks 2(6):568–576Google Scholar
  41. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  42. Verleysen M, Hlavackova K (1994) An optimized RBF network for approximation of functions. Proceedings European Symposium on Artificial Neural Networks. Brussels, Belgium, pp 175–180Google Scholar
  43. Yang CT (1996) Sediment transport theory and practice. McGraw-Hill, USAGoogle Scholar
  44. Yanmaz M, Kumcu SY (2007) Measurement of sediment load. Post-graduate course in sediment transport technology. DSI press, AnkaraGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of SelcukKonyaTurkey
  2. 2.Department of Civil EngineeringUniversity of Necmettin ErbakanKonyaTurkey

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