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Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

  • Banu Yilmaz
  • Egemen ArasEmail author
  • Murat Kankal
  • Sinan Nacar
Research Article - Hydrology
  • 31 Downloads

Abstract

The main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching–learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, İnanlı and Altınsu, in Çoruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.

Keywords

Artificial bee colony Çoruh river basin Estimation Suspended sediment loading Teaching–learning-based optimization 

Notes

Acknowledgments

The authors thank YEGM (General Directorate of Renewable Energy) for the hydrological data of the research. This study is dedicated in memory of the late Assoc. Prof. Dr. Murat İhsan KÖMÜRCÜ, who died in February 2013.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Akpınar A, Kömürcü Mİ, Kankal M (2011) Development of hydropower energy in Turkey: the case of Coruh river basin. Renew Sustain Energy Rev 15(2):1201–1209Google Scholar
  2. Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22(1):2–13Google Scholar
  3. Altunkaynak A (2009) Sediment load prediction by genetic algorithms. Adv Eng Softw 40(9):928–934Google Scholar
  4. Anderson JA., Rosenfeld E, Pellionisz A (eds) (1988). Neurocomputing (vol 2). MIT Press, CambridgeGoogle Scholar
  5. Ardıclıoglu M, Kişi Ö, Haktanır T (2007) Suspended sediment prediction using two different feed-forward back-propagation algorithms. Can J Civ Eng 34(1):120–125Google Scholar
  6. ASCE Task Committee (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123Google Scholar
  7. Berkun M (2010) Hydroelectric potential and environmental effects of multidam hydropower projects in Turkey. Energy Sustain Dev 14(4):320–329Google Scholar
  8. Cigizoglu HK (2001) Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turk J Eng Environ Sci 26(1):27–36Google Scholar
  9. Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27(2):185–195Google Scholar
  10. Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398(3):292–302Google Scholar
  11. Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367(1):52–61Google Scholar
  12. Črepinšek M, Liu SH, Mernik L (2012) A note on teaching–learning-based optimization algorithm. Inf Sci 212:79–93Google Scholar
  13. Dede T (2013) Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization. Struct Multidisci Optim 48(5):955–964Google Scholar
  14. DSI (General Directorate of State Hydraulic Works) (2006) Yusufeli dam and hydropower plant project, Chapter I: introduction. Environmental Impact Assessment, Draft Final Report, AnkaraGoogle Scholar
  15. DSI (General Directorate of State Hydraulic Works) (2009) Coruh River development plan. In: International workshop on transboundary water resources management; Tbilisi, GeorgiaGoogle Scholar
  16. Gershenson C (2003) Artificial neural networks for beginners. arXiv preprint cs/0308031.Google Scholar
  17. Hassim YMM, Ghazali R (2012) Training a functional link neural network using an artificial bee colony for solving a classification problems. arXiv preprint arXiv:1212.6922.Google Scholar
  18. Howell DC (1998) Statistical methods in human sciences. Wadsworth, New YorkGoogle Scholar
  19. Jain SK (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng 127(1):30–37Google Scholar
  20. Jain SK, Jha R (2005) Comparing the stream re-aeration coefficient estimated from ANN and empirical models/Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau. Hydrol Sci J 50(6):1037–1052Google Scholar
  21. Kankal M, Uzlu E (2017) Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput Appl 28:737–747Google Scholar
  22. Kankal M, Akpınar A, Kömürcü Mİ, Özşahin TŞ (2011) Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl Energy 88(5):1927–1939Google Scholar
  23. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
  24. Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346(4):328–348Google Scholar
  25. Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209:1–15Google Scholar
  26. Khoshnevisan B, Rafiee S, Omid M, Yousefi M, Movahedi M (2013) Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy 52:333e8Google Scholar
  27. Kisi Ö (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329(3):636–646Google Scholar
  28. Kisi Ö (2008) Constructing neural network sediment estimation models using a data-driven algorithm. Math Comput Simul 79(1):94–103Google Scholar
  29. Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456:110–120Google Scholar
  30. Kisi O, Shiri J (2012) River suspended sediment estimation by climatic variables implication: comparative study among soft computing techniques. Comput Geosci 43:73–82Google Scholar
  31. Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40(6):438–444Google Scholar
  32. Kisi O, Ozkan C, Akay B (2012) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428:94–103Google Scholar
  33. Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62Google Scholar
  34. McBean EA, Al-Nassri S (1988) Uncertainty in suspended sediment transport curves. J Hydraul Eng 114(1):63–74Google Scholar
  35. Miller J (1991) Reaction time analysis with outlier exclusion: bias varies with sample size. Q J Exp Psychol 43(4):907–912Google Scholar
  36. Niknam T, Azizipanah-Abarghooee R, Narimani MR (2012) A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Eng Appl Artif Intell 25(8):1577–1588Google Scholar
  37. Nourani V, Sharghi E, Aminfar MH (2012) Integrated ANN model for earthfill dams seepage analysis: Sattarkhan dam in Iran. Artif Intell Res 1:22e37Google Scholar
  38. Öcal O (2007) Yapay sinir ağları algoritması kullanılarak akarsu havzalarında yağış-akış-katı madde ilişkisinin belirlenmesi (in Turkish), Dissertation, Pamukkale UniversityGoogle Scholar
  39. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci 181(12):2455–2468Google Scholar
  40. Patel VK, Savsani VJ (2016) A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Inf Sci 357:182–200Google Scholar
  41. Pour OMR, Shui LT, Dehghani AA (2011) Genetic algorithm model for the relation between flow discharge and suspended sediment load (Gorgan river in Iran). Electron J Geotech Eng 16:539–553Google Scholar
  42. Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nourani V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci Total Environ 407(17):4916–4927Google Scholar
  43. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315Google Scholar
  44. Sajwan N, Rajesh K (2011) Designing aspects of artificial neural network controller. Int J Sci Eng Res 2–4Google Scholar
  45. Satapathy SC, Naik A (2011) Data clustering based on teaching-learning-based optimization. In: International conference on swarm, evolutionary, and memetic computing (pp 148–156). Springer, BerlinGoogle Scholar
  46. Schulze FH, Wolf H, Jansen HW, Van der Veer P (2005) Applications of artificial neural networks in integrated water management: fiction or future? Water Sci Technol 52(9):21–31Google Scholar
  47. Tayfur G (2002) Artificial neural networks for sheet sediment transport. Hydrol Sci J 47(6):879–892Google Scholar
  48. Tayfur G, Guldal V (2006) Artificial neural networks for estimating daily total suspended sediment in natural streams. Hydrol Res 37(1):69–79Google Scholar
  49. Toğan V (2012) Design of planar steel frames using teaching–learning based optimization. Eng Struct 34:225–232Google Scholar
  50. Uckardes F, Sahinler S, Efe E (2010) Aykırı gözlemlerin belirlenmesinde kullanilan bazi istatistikler(in Turkish). KSU J Nat Sci 13(1):42–45Google Scholar
  51. Uzlu E, Akpınar A, Özturk HT, Nacar S, Kankal M (2014a) Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy 69:638–647Google Scholar
  52. Uzlu E, Kömürcü Mİ, Kankal M, Dede T, Öztürk HT (2014b) Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Appl Ocean Res 48:103–113Google Scholar
  53. Uzlu E, Kankal M, Akpınar A, Dede T (2014c) Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy 75:295–303Google Scholar
  54. Yeh WC, Hsieh TJ (2012) Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation. Neural Comput Appl 21(2):365–375Google Scholar
  55. Yilmaz B, Aras E, Nacar S (2016) Estimation of daily suspended sediment load with an artificial neural network. In: 1st International Black Sea congress on environmental sciences (IBCESS), pp 708–720Google Scholar
  56. Yilmaz B, Aras E, Nacar S, Kankal M (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840Google Scholar
  57. Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761–4767Google Scholar
  58. Zhu YM, Lu XX, Zhou Y (2007) Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment. China Geomorphol 84(1):111–125Google Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of TechnologyKaradeniz Technical UniversityTrabzonTurkey
  2. 2.Department of Civil Engineering, Faculty of Engineering and Natural SciencesBursa Technical UniversityBursaTurkey
  3. 3.Department of Civil Engineering, Faculty of EngineeringBursa Uludağ UniversityBursaTurkey
  4. 4.Department of Civil Engineering, Faculty of EngineeringKaradeniz Technical UniversityTrabzonTurkey

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