Skip to main content

Advertisement

SpringerLink
Log in

Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region, Turkey

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This study investigates the appropriateness of four major empirical methods [Lane and Kalinske, Einstein, Brooks, Chang—Simons—Richardson] for predicting suspended sediment loads (SSLs) in three major rivers in the Aegean Region, Turkey. The measured data from 1975 to 2005 were used to test performance of the models. It was found that Brooks method was more appropriate, among the others, for predicting suspended sediment loads from each river. The prediction results of Brooks method were further improved by the use of genetic algorithm (GA_Brooks) optimizing a fitting parameter and showing a comparable performance to those of artificial neural networks (ANNs) and neuro-fuzzy (ANFIS) models for the same rivers. GA_Brooks, ANNs, and ANFIS models can be used for predicting loads at a regional scale. The sensitivity analysis results revealed that suspended and bed material particle diameters affect suspended sediment loads significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Alp M, Cığızoğlu HK (2007) Suspended sediment load estimation by two artificial neural network methods using hydro meteorological data. Environ Model Softw 22:2–13

    Article  Google Scholar 

  • ASCE (2000) Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Eng. ASCE 5(2):115–123

    Google Scholar 

  • Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modelling. J Hydrol 351:288–298

    Article  Google Scholar 

  • Camenen B, Larson M (2008) A general formula for noncohesive suspended sediment transport. J Coast Res 24(3):615–627

    Article  Google Scholar 

  • Chaudhry MH (1993) Open-channel flow. Prentice Hall, Englewood Cliffs, p 523

    Google Scholar 

  • Cybenco G (1989) Approximation by superposition of a sigmoidal function. Math Control Signals Syst 2:303–314

    Article  Google Scholar 

  • Dietrich WE (1982) Settling velocity of natural parts. Water Resour Res 18(6):1615–1626

    Article  Google Scholar 

  • DMI (2010) Turkish state meteorological service, www.meteor.gov.tr

  • DSI (2010) General directorate of state hydraulic works, www.dsi.gov.tr

  • EIE (2006) Suspended sediment data for surface waters in Turkey. General Directorate of Electrical Power Resources Survey and Development Administration, Ankara

    Google Scholar 

  • EIE (2010) General directorate of electrical power resources survey and development administration, www.eie.gov.tr

  • Girma NT, Horlacher HB (2004) Investigation of performance of sediment transport formulas in natural rivers based on measured data in Kulfo River, Southern Ethiopia. FWU, Vol. 4, Lake Abaya Research Symposium, Proceedings

  • Goldberg DE (1989) Genetic algorithms for search, optimization, and machine learning. Addison-Wesley, USA

    Google Scholar 

  • Goldberg DE (1999) Genetic Algorithms. Addison-Wesley, USA

    Google Scholar 

  • Graf WH (1984) Hydraulics of sediment transport. Water Resources Publications, Colorado, p 513

    Google Scholar 

  • Gray J, Fisk G (1992) Monitoring radionuclide and suspended sediment transport in the Little Colorado Rover Basin, Arizona and New Mexico, USA. Proceedings of the Symposium Erosion and Sediment Transport Monitoring Programmes in River Basins. Oslo, IAHS publ. No. 210:505–516

  • Guan J, Aral MM (2005) Remediation system design with multiple uncertain parameters using fuzzy sets and genetic algorithm. J Hydrol Eng 10(5):386–394

    Article  Google Scholar 

  • Guven A, Kisi O (2011) Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming. Water Resour Manag 25:691–704

    Article  Google Scholar 

  • Haykin S (1998) Neural networks – A comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26–32

    Google Scholar 

  • Horowitz AJ (1997) Some thoughts on problems associated with various sampling media used for environmental monitoring. Analyst 122:1193–1200

    Article  Google Scholar 

  • Jain A, Bhattacharjya RK, Sanaga S (2004) Optimal design of composite channels using genetic algorithm. J Irrig Drain Eng 130(4):286–295

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:65–85

    Article  Google Scholar 

  • Jimanez JA, Madson OS (2003) A simple formula to estimate settling velocity of natural sediments. J Watervay Port, Coastal Ocean Eng 129(2):70–78

    Article  Google Scholar 

  • Julien PY (2002) River mechanics. Cambridge University Pres, Cambridge, p 434

    Book  Google Scholar 

  • Kavvas ML, Yoon J, Chen ZQ, Liong L, Dogrul EC, Ohara N, Aksoy H, Anderson ML, Reuter J, Hackley S (2006) Watershed environmental hydrology model: environmental module and its application to a California watershed. J Hydrol Eng ASCE 11(3):261–272

    Article  Google Scholar 

  • Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456–457:110–120

    Article  Google Scholar 

  • Kisi O, Shiri J (2012) River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques. Comput Geosci 43:73–82

    Article  Google Scholar 

  • Kisi O, Yuksel I, Dogan E (2008) Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrol Sci J 53(6):1270–1285

    Article  Google Scholar 

  • Kisi O, Dailr AH, Çimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58

    Article  Google Scholar 

  • Ladewig MD (2006) Sediment transport rates in the Lower Muskegon River and Tributaries. MSc Thesis. University of Michigan, Natural Resources and Environment, USA

  • Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62

    Article  Google Scholar 

  • Lee G-S, Lee K-H (2010) Determining the sediment delivery ratio using a sediment-rating curve and a geographic information system—embedded soil erosion model on a basin scale. J Hydrol Eng ASCE 15(10):834–843

    Article  Google Scholar 

  • Liong SY, Chan WT, Shreeram J (1995) Peak flow forecasting with genetic algorithm and SWMM. J Hydraul Eng ASCE 121(8):613–617

    Article  Google Scholar 

  • Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588–595

    Article  Google Scholar 

  • Nourani V, Andalib G (2015) Daily and monthly suspended load predictions using wavelet based artificial intelligence approaches. J Mt Sci 12(1):85–100

    Article  Google Scholar 

  • 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 30:393–407

    Article  Google Scholar 

  • Olive LJ, Rieger WA (1988) On examination of the role of sampling strategies in the study of the suspended sediment transport. Sediment Budgets, Proceedings of the Porto alegre Symposium, IAHS Publ., No. 174:259–267

  • Ozturk F, Apaydın H, Walling DE (2001) Suspended sediment loads through flood events for streams of Sakarya River Basin. Turk J Eng Environ Sci 25:643–650

    Google Scholar 

  • Partal T, Cigizoglu HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J Hydrol 358:317–331

    Article  Google Scholar 

  • Picouet C, Hingray B, Olivry JC (2001) Empirical and Conceptual modeling of the suspended sediment dynamics in a large tropical African river: The upper Niger River basin. J Hydrol 250:19–39

    Article  Google Scholar 

  • Rajaee T (2011) Wavelet and ANN combinatiom model for prediction of daily suspended sediment load in rivers. Sci Total Environ 409:2917–2928

    Article  Google Scholar 

  • Rojas R, Velleux M, Julien PY, Johnson BE (2008) Grid scale effects on watershed soil erosion models. J Hydrol Eng ASCE 13(9):793–802

    Article  Google Scholar 

  • Sen Z (2004) Genetic algorithm and optimization methods. Su Vakfi Yayinlari, Istanbul. (Turkish), ISBN: 975-6455-12-8

  • Sen Z, Oztopal A (2001) Genetic algorithms for the classification and prediction of precipitation occurrence. Hydrol Sci J 46(2):255–267

    Article  Google Scholar 

  • Simons DB, Senturk F (1992) Sediment transport technology, water and sediment dynamics. Water Resources Publications, Colorado, p 897

    Google Scholar 

  • Singh RM, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng ASCE 11(2):101–109

    Article  Google Scholar 

  • Singh VP, Tayfur G (2008) Kinematic wave theory for transient bed sediment waves in alluvial rivers. J Hydrol Eng ASCE 13(5):297–304

    Article  Google Scholar 

  • Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng ASCE 7(4):270–292

    Article  Google Scholar 

  • Tayfur G (2003) Modelling sediment transport. In: Singh VP, Yadava RN (eds) Watershed Hydrology. Allied Publishers, p 353–375

  • Tayfur G (2009) GA-optimized model predicts dispersion coefficient in natural channels. Hydrol Res 40(1):65–78

    Article  Google Scholar 

  • Tayfur G (2012) Soft computing in water resources engineering; artificial neural networks, fuzzy logic and genetic algorithm. WIT Press, Southampton, p 267

    Google Scholar 

  • Tayfur G, Guldal V (2006) Artificial Neural Networks for estimating daily total suspended sediment in natural streams. Nord Hydrol 37(1):69–79

    Google Scholar 

  • Tayfur G, Moramarco T (2008) Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms. J Hydrol 352(1–2):77–93

    Article  Google Scholar 

  • Tayfur G, Singh VP (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. J Hydraul Eng ASCE 132(12):1321–1330

    Article  Google Scholar 

  • Tayfur G, Singh VP (2007) Kinematic wave model for transient bed profiles in alluvial channels under nonequilibrium conditions. Water Resour Res 43, W12412

    Article  Google Scholar 

  • Tayfur G, Singh VP (2011) Predicting mean and bankfull discharge from channel cross-sectional area by expert and regression methods. Water Resour Manag 25(5):1253–1267

    Article  Google Scholar 

  • Tayfur G, Barbetta S, Moramarco T (2009) Genetic Algorithm-based discharge estimation at sites receiving lateral inflows. J Hydrol Eng ASCE 14(5):463–474

    Article  Google Scholar 

  • Ulke A (2010) Application of empirical, regression and artificial intelligence methods for the sediment transport in natural streams of the aegean region. PhD Thesis (in Turkish), Dokuz Eylul University, The Graduate School of Natural and Applied Science, Izmir

  • Ulke A, Tayfur G, Ozkul S (2009) Predicting suspended sediment loads and missing data for Gediz River, Turkey. J Hydrol Eng ASCE 14(9):954–965

    Article  Google Scholar 

  • Ulke A, Ozkul S, Tayfur G (2011) Empirical methods for predicting suspended sediment load in Gediz River. IMO Teknik Dergi 22(2):5387–5407

    Google Scholar 

  • Woitke P, Wellmitz J, Helm D, Kube P, Lepom P, Litheraty P (2003) Analysis and assessment of heavy metal pollution in suspended solids and sediments of the river Danube. Chemosphere 51:633–642

    Article  Google Scholar 

  • Wu W (2004) Depth-averaged two-dimensional numerical modeling of unsteady flow and nonuniform sediment transport in open channels. J Hydraul Eng ASCE 130(10):1013–1024

    Article  Google Scholar 

  • Wu W, Vierira DA, Wang SSY (2004) One-dimensional numerical model for nonuniform sediment transport under unsteady flows in channel networks. J Hydraul Eng ASCE 130(9):914–923

    Article  Google Scholar 

  • Yang CT (1996) Sediment transport theory and practice. McGraw-Hill, USA, p 897

    Google Scholar 

  • Yang CT, Molinas A, Wu B (1996) Sediment transport in the Yellow River. J Hydraul Eng ASCE 122(5):237–244

    Article  Google Scholar 

  • Yang CT, Marsooli R, Aalami T (2009) Evaluation of total sediment transport formulas using ANN. Int J Sediment Res 24:274–286

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank EIE [General Directorate of Electrical Power Resources Survey and Development Administration] for providing the flow and sediment data and DMI [Turkish State Meteorological Service] for providing the precipitation data. The authors would also like to thank to the Geotechnical and Environmental laboratories of the Dokuz Eylul University for the analysis of the field samples.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Engineering Faculty, Ondokuz Mayis University, Samsun, Turkey

    Asli Ulke

  2. Department of Civil Engineering, Engineering Faculty, Izmir Institute of Technology, Urla, Izmir, Turkey

    Gokmen Tayfur

  3. Department of Civil Engineering, Engineering Faculty, Dokuz Eylul University, Izmir, Turkey

    Sevinc Ozkul

Authors
  1. Asli Ulke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gokmen Tayfur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sevinc Ozkul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asli Ulke.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ulke, A., Tayfur, G. & Ozkul, S. Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region, Turkey. Water Resour Manage 31, 739–764 (2017). https://doi.org/10.1007/s11269-016-1357-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1357-z

Keywords

Search

Navigation