Neural Computing and Applications

, Volume 23, Issue 3–4, pp 873–880 | Cite as

A practical approach to formulate stage–discharge relationship in natural rivers

Original Article

Abstract

This study proposes a new formulation technique for modeling stage–discharge relationship, as an alternative approach to standard regression techniques. An explicit neural network formulation (ENNF) is derived by using data obtained from United States Geological Survey data base. The neural network model is trained and tested using time series of daily stage and discharge data from two stations in Pennsylvania, USA. The model is compared with the standard rating curve (SRC) technique. Statistical parameters such as average, standard deviation, minimum, and maximum values, as well as criteria such as root mean square error, the efficiency coefficient (E), and determination coefficient (R2) are used to measure the performance of the ENNF. Considerably, well performance is achieved in modeling streamflow by using ENNF. The comparison results reveal that the suggested formulations perform better than the conventional SRC.

Keywords

Stage discharge Neural networks Rating curve Modeling 

References

  1. 1.
    Ab. Ghani A, Chang CK, Leow CS, Zakaria NA (2012) Sungai Pahang digital flood mapping: 2007 flood. Int J River Basin Manag 10(2):139–148CrossRefGoogle Scholar
  2. 2.
    Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control AC-19:716–723MathSciNetCrossRefGoogle Scholar
  3. 3.
    Alavi AH, Gandomi AH, Mollahasani A, Heshmati AAR, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379CrossRefGoogle Scholar
  4. 4.
    Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89(23–24):2176–2194CrossRefGoogle Scholar
  5. 5.
    ASCE Task Committee (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123CrossRefGoogle Scholar
  6. 6.
    ASCE Task Committee (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137CrossRefGoogle Scholar
  7. 7.
    Azamathulla HM, Ghani A, Leow CS, Chang CK, Zakaria NA (2011) Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water Resour Manag 25(11):2901–2916CrossRefGoogle Scholar
  8. 8.
    Bhattacharya B, Solomatine DP (2000) Application of artificial neural network in stage-discharge relationship. In: Proceedings of 4th international conference on hydroinformatics. IAHR, Iowa CityGoogle Scholar
  9. 9.
    Fread DL (1973) A dynamic model of stage-discharge relations affected by changing discharge. NOAA Tech. Memo. NWS HYDRO-16, National Weather Service, Silver SpringGoogle Scholar
  10. 10.
    Fread DL (1975) Computation of stage-discharge relationships affected by unsteady flow. Water Resour Bull 11(2):213–228CrossRefGoogle Scholar
  11. 11.
    Gandomi AH, Alavi AH, Mirzahosseini MR, Moqhadas Nejad F (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civ Eng ASCE 23(3):248–263CrossRefGoogle Scholar
  12. 12.
    Gandomi AH, Tabatabaie SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for load capacity of castellated steel beams. J Constr Steel Res 67(7):1096–1105CrossRefGoogle Scholar
  13. 13.
    Gandomi AH, Babanajad SK, Alavi AH, Farnam Y (2012) A novel approach to strength modeling of concrete under triaxial compression. J Mater Civ Eng ASCE (in press). doi:10.1061/(ASCE)MT.1943-5533.0000494
  14. 14.
    Gandomi AH, Alavi AH (2011) Applications of computational ıntelligence in behavior simulation of concrete materials. In: Yang XS, Koziel S (eds) Chapter 9 in computational optimization and applications in engineering and industry, vol 359. Springer SCI, pp 221–243Google Scholar
  15. 15.
    Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239CrossRefGoogle Scholar
  16. 16.
    Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21(1):189–201CrossRefGoogle Scholar
  17. 17.
    Goh ATC, Kulhawy FH, Chua CG (2005) Bayesian neural network analysis of undrained side resistance of drilled shafts. J Geotech Geoenviron Eng 131(1):84–93CrossRefGoogle Scholar
  18. 18.
    Guven A, Gunal M, Cevik AK (2006) Prediction of pressure fluctuations on stilling basins. Can J Civ Eng 33(11):1379–1388CrossRefGoogle Scholar
  19. 19.
    Guven A, Aytek A, Yuce MI, Aksoy H (2007) Genetic programming-based empirical model for daily reference evapotranspiration estimation. Clean-Soil Air Water 36(10–11):905–912Google Scholar
  20. 20.
    Guven A, Aytek A (2009) A new approach for stage-discharge relationship: gene-expression programming. J Hydrol Eng 14(8):812–820CrossRefGoogle Scholar
  21. 21.
    Haykin S (1999) Neural networks: a comprehensive foundation. Pearson Education Inc., New JerseyMATHGoogle Scholar
  22. 22.
    Jain SK, Chalisgaonkar D (2000) Setting up stage-discharge relations using ANN. J Hydrol Eng 5(4):428–433CrossRefGoogle Scholar
  23. 23.
    Legates DR, McCabe GJ (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  24. 24.
    Liao H, Knight DW (2007) Analytic stage–discharge formulae for flow in straight trapezoidal open channels. Adv Water Resour. doi:10.1016/j.advwatres.2007.05.002 Google Scholar
  25. 25.
    Liong SY, Lim W, Paudyal GN (2000) River stage forecasting in Bangladesh: neural network approach. J Comput Civ Eng 14(1):1–18CrossRefGoogle Scholar
  26. 26.
    Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage–discharge–sediment concentration relationships using fuzzy logic. Hydrol Sci 52(4):793–807CrossRefGoogle Scholar
  27. 27.
    Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15(1):101–124CrossRefGoogle Scholar
  28. 28.
    Overleir P (2006) Modelling stage–discharge relationships affected by hysteresis using the Jones formula and nonlinear regression. Hydrol Sci 51(3):365–388CrossRefGoogle Scholar
  29. 29.
    Overleir P (2006) A robust stage-discharge rating curve model based on critical flow from a reservoir. Hydrol Res 37(3):217–233CrossRefGoogle Scholar
  30. 30.
    Panagoulia D (2006) Artificial neural networks and high and low flows in various climate regimes. Hydrol Sci 51(4):563–587CrossRefGoogle Scholar
  31. 31.
    Schmidt AR, Yen BC (2002) Stage-discharge ratings revisited. In: Wahl TL, Pugh CA, Oberg KA, Vermeyen TB (eds) Hydraulic measurements and experimental methods, Proceedings of EWRI and IAHR joint conference, Estes ParkGoogle Scholar
  32. 32.
    Sivapragasam C, Mutill N (2005) Discharge rating curve extension—a new approach. Water Resour Manag 19:505–520CrossRefGoogle Scholar
  33. 33.
    Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng 8(3):161–164CrossRefGoogle Scholar
  34. 34.
    Supharatid S (2003) Application of a neural network model in establishing a stage-discharge relationship for a tidal river. Hydrol Process 17(15):3085–3099CrossRefGoogle Scholar
  35. 35.
    Tawfik M, Ibrahim A, Fahmy H (1997) Hysteresis sensitive neural network for modeling rating curves. J Comput Civ Eng 11(3):206–211CrossRefGoogle Scholar
  36. 36.
    Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3(1):26–32CrossRefGoogle Scholar
  37. 37.
    Torsten D, Gerd M, Torsten S (2002) Extrapolating stage-discharge relationships by numerical modeling. In: International conference on hydraulic engineering, Warshaw, pp 1–8Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of GaziantepGaziantepTurkey
  2. 2.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong TebalMalaysia
  3. 3.Sahinbey MunicipalityGaziantepTurkey

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