Skip to main content
SpringerLink
Log in

Flow estimations for the Sohu Stream using artificial neural networks

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN) method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage, current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the current flow). The coefficient of determination (R 2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R 2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. In all models, the variables with the highest R 2 ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Alp M, Cigizoglu HK (2004) Modelling of rainfall–runoff relation with different ANN methods. ITU Dergisi/d Muhendislik 3(1):80–88 (in Turkish)

    Google Scholar 

  • Anctil F, Perrin C, Andreassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall–runoff forecasting models. J Environ Models Softw 19:357–368

    Article  Google Scholar 

  • Apaydin H, Ozturk F (2008) Modelling short-term flow estimations for the Sohu Stream via artificial neural networks. TUBITAK Research (107O911) Report, Ankara (in Turkish)

  • Apaydın H, Ozturk F (2003) Application of runoff and sediment yield models by geographic information system. J Agric Sci 9(4):381–389

    Google Scholar 

  • Apaydin H, Sonmez FK, Yildirim YE (2004) Spatial interpolation techniques for climate data in the GAP region in Turkey. Clim Res 28:31–40

    Article  Google Scholar 

  • Boogaard H, Gautam DK, Mynett AE (1998) Auto-regressive neural networks for the modelling of time series. In: Hydroinformatics conference, Copenhagen

  • Burak S, Duranyıldız I, Yetis U (1997) National environmental act plan: Management of water resources. DPT, Ankara (in Turkish), pp 1–106

  • Brikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7(5):392–398

    Article  Google Scholar 

  • Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35:1191–1197

    Article  Google Scholar 

  • Chang TC, Wang ZY, Chien YH (2010) Hazard assessment model for debris flow prediction. Environ Earth Sci 60:1619–1630

    Article  Google Scholar 

  • Cigizoglu HK (2002a) Suspended sediment estimation and forecasting using artificial neural networks. Turk J Eng Environ Sci 26:15–25

    Google Scholar 

  • Cigizoglu HK (2002b) Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turk J Eng Environ Sci 26:27–36

    Google Scholar 

  • Cigizoglu HK (2003a) Estimation, forecasting and extrapolation of flow data by artificial neural networks. J Hydrol Sci 48(3):349–361

    Article  Google Scholar 

  • Cigizoglu HK (2003b) Incorporation of ARMA models into flow forecasting by artificial neural networks. Environmetrics 14(4):417–427

    Article  Google Scholar 

  • Cigizoglu HK (2005) Generalized regression neural networks in monthly flow forecasting. Civil Eng Environ Syst 22(2):71–84

    Article  Google Scholar 

  • Cigizoglu HK, Kisi O (2005) Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. J Nord Hydrol 36(1):1–16

    Google Scholar 

  • Coulibaly P, Anctil F, Bobée B (1998) Real time neural network based forecasting system for hydropower reservoirs. In: Proceedings of the first international conference on new information. Technologies for decision making in civil engineering, University of Quebec, Montreal, Canada, pp 1001–1011

  • De Vos NJ, Rientjes THM (2007) Multi-objective performance comparison of an artificial neural network and a conceptual rainfall–runoff model. Hydrol Sci J 52(3):397–413

    Article  Google Scholar 

  • Djebbar Y, Alila Y (1998) Neural network estimation of sanitary flows. In: Hydroinformatics conference, Copenhagen

  • Dongquan Z, Jining C, Haozheng W, Qingyuan T, Shangbing C, Zheng S (2009) GIS-based urban rainfall–runoff modeling using an automatic catchment—discretization approach: a case study in Macau. Environ Earth Sci 59:465–472

    Article  Google Scholar 

  • Dou Y, Chen X, Bao A, Li L (2011) The simulation of snow melt runoff in the ungauged Kaidu River Basin of Tian Shan Mountains, China. Environ Earth Sci 62:1039–1045

    Article  Google Scholar 

  • EIE (2006) EIE annual flow report, Ankara (in Turkish)

  • Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional ground water levels affected by human activities. Ground Water 46(1):80–90

    Google Scholar 

  • Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209

    Article  Google Scholar 

  • Graupe D (2007) Principles of artificial neural networks, 2nd edn. World Scientific Publishing, Singapore, pp 1–320

    Google Scholar 

  • Gwo-Fong L, Chen GR (2008) A systematic approach to the input determination for neural network rainfall–runoff models. Hydrol Process 22(14):2524–2530

    Article  Google Scholar 

  • Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modelling of the rainfall runoff process. Water Resour Res 31:2517–2530

    Article  Google Scholar 

  • Jain SK (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng 9(1):60–63

    Google Scholar 

  • Jain SK, Das D, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag 125(5):263–271

    Google Scholar 

  • Jain A, Indurthy SKVP (2003) Comparative analysis of event-based rainfall–runoff modeling techniques—deterministic, statistical, and artificial neural networks. J Hydrol Eng 8(2):93–98

    Article  Google Scholar 

  • Karahan H, Ilhan MH, Ayvaz MT (2007) Predicting daily missing discharges using artificial neural network and genetic algorithm: an application for Curuksu (Buyuk Menderes) basin. In: V. National hydrology congress, Middle East Technical University, Ankara (in Turkish)

  • KHGM (1999) KHGM Findikli-Sohu Creek (1253) Basin research report No. 57, Ankara (in Turkish)

  • Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63

    Article  Google Scholar 

  • Kisi O, Nas N (2007) River suspended sediment estimation using artificial intelligence methods. In: V. National hydrology congress, Middle East Technical University, Ankara (in Turkish)

  • Lange N (1998) Advantages of unit hydrograph derivation by neural networks. In: Hydroinformatics conference, Copenhagen

  • Mason JC, Price RK, Tem’me A (1996) A neural network model of rainfall runoff using radial basis functions. J Hydraul Res 34(4):537–548

    Article  Google Scholar 

  • McMath RE (1887) Determination of the size of sewers. Trans Am Soc Civil Eng 16:179–190

    Google Scholar 

  • Minns AW, Hall MJ (1996) Artificial neural networks as rainfall runoff models. J Hydrol Sci 41(3):399–417

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I: a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • NeuroSolutions (2008) NeuroSolutions getting started manual version 5

  • Rabuñal JR, Dorado J (2006) Artificial neural networks in real life applications, pp 1–395

  • Raman H, Sunilkumar N (1995) Multivariate modeling of water resources time series using artificial neural networks. J Hydrol Sci 40(2):145–163

    Article  Google Scholar 

  • Ranjithan S, Eheart JW, Garrett JH (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour Res 29(3):563–574

    Article  Google Scholar 

  • Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481

    Article  Google Scholar 

  • Rumelhart DE, McLelland JL, PDP Research Group (1986) Parallel distributed processing: Explorations in the micro structure of cognition. Vol. I: Foundations, MIT, Cambridge, pp 1–564

  • Sattari MT, Fakher-Fard A, Docherkhesaz A, Ozturk F (2007) Simulation of Savalan irrigation reservoir by using artificial neural networks. J Hydrol 214:32–48

    Google Scholar 

  • Sattari MT, Apaydin H, Ozturk F (2009) Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation. Stoch Env Res Risk Assess 23(8):1187–1201

    Article  Google Scholar 

  • Sertel E, Cigizoglu HK, Sanli DU (2008) Estimating daily mean sea level heights using artificial neural networks. J Coast Res 24(3):727–734

    Article  Google Scholar 

  • Shamseldin AY (1997) Application of a neural network technique to rainfall–runoff modeling. J Hydrol 199:272–294

    Article  Google Scholar 

  • Sreekanth PD, Sreedevi PD, Ahmed S, Geethanjali N (2011) Comparison of FFNN and ANFIS models for estimating groundwater level. Environ Earth Sci 62:1301–1310

    Article  Google Scholar 

  • Thirumalaiah K, Deo MC (1998) Real-time flood forecasting using neural networks. J Comput Aided Civil Infrastruct Eng 13(2):101–111

    Article  Google Scholar 

  • Tokar AS, Johnson PA (1999) Rainfall runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239

    Article  Google Scholar 

  • Ulke A, Ozkul S, Tayfur G (2007) Determination of suspended bed load in Gediz River via ANN. In: V. National hydrology congress, Orta Dogu Teknik University, Ankara (in Turkish)

  • United States Department of Agriculture (1986) Urban hydrology for small watersheds. Technical release 55 (TR-55), second edn. Natural Resources Conservation Service, Conservation Engineering Division

  • Yurtoglu H (2005) Forecasting with neural networks modeling methodology. Economic Models and Strategic Research, Ankara

  • Zealand CM, Burn DH, Simonovic SP (1999) Short-term stream flow forecasting using artificial neural networks. J Hydrol 214:32–48

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with a project number of 107O911.

Author information

Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    M. Taghi Sattari

  2. Department of Farm Structures and Irrigation, Faculty of Agriculture, University of Ankara, 06110, Diskapi, Ankara, Turkey

    Halit Apaydin & Fazli Ozturk

Authors
  1. M. Taghi Sattari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Halit Apaydin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fazli Ozturk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Halit Apaydin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sattari, M.T., Apaydin, H. & Ozturk, F. Flow estimations for the Sohu Stream using artificial neural networks. Environ Earth Sci 66, 2031–2045 (2012). https://doi.org/10.1007/s12665-011-1428-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-011-1428-7

Keywords

Search

Navigation