Neural Computing and Applications

, Volume 23, Supplement 1, pp 73–82 | Cite as

Fuzzy genetic approach for modeling of the critical submergence of an intake

  • Fikret Kocabaş
  • Burhan Ünal
  • Serap Ünal
  • Halil İbrahim Fedakar
  • Ercan Gemici
Original Article
First Online:
Received:
Accepted:
  • 248 Downloads

Abstract

The vertical distance between the water level and upper level of intake is called submergence. When the submergence of the intake pipe is not sufficient, air enters the intake pipe and reduction in discharge occurs. The submergence depth at which incipient air entrainment occurs at a pipe intake is called the critical submergence (S c ). It can also cause mechanical damage, vibration in pipelines and loss of pump performance. Therefore, the determination of the S c value is a significant problem in hydraulic engineering. To estimate the S c values for different pipe diameters, experimental works are conducted and results obtained are used for modeling of critical submergence ratio (S c /D i ). In this study, a fuzzy genetic (FG) approach is proposed for modeling of the S c /D i . The channel flow velocity (U), intake pipe velocity (V i ) and porosity (n) are used as input variables, and the critical submergence ratio (S c /D i ) is used as output variable. The 44 data sets obtained by experimental work were divided into two parts and 28 data sets (approximately 64 %) were used for training, and 16 data sets (approximately 36 %) were used for testing of models. The experimental results were compared with FG, an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs). The comparison revealed that the FG models outperformed the ANFIS and ANN in terms of root mean square error (RMSE) and determination coefficient (R 2) statistics for the data sets used in this study. In addition to RMSE and R 2, which are used as main model evaluation criteria, mean absolute error is used to evaluate the performance of models.

Keywords

Fuzzy genetic approach Adaptive neuro-fuzzy inference system Artificial neural network Critical submergence ratio Intake pipe 

List of symbols

c (m)

Clearance (vertical distance of intake to bottom of tank)

Di (m)

Internal diameter of intake pipe

h (m)

Uniform flow depth

n

Porosity

Qi (m3/s)

Intake discharge

Q0 (m3/s)

Channel discharge

Sc (m)

Critical submergence

Sc/Di

Critical submergence ratio

U (m/s)

Channel flow velocity

Vi (m/s)

Intake pipe velocity

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

References

  1. 1.
    Kocabas F, Yildirim N, Donmez S (2010) An artificial neural networks model for the circulation imposed on critical submergence of an intake pipe. Kuwait J Sci Eng 37:21–34Google Scholar
  2. 2.
    Denny DF (1956) An experimental study of air entraining vortices in pump sumps. Proc Inst Mech Eng 170:106–116CrossRefGoogle Scholar
  3. 3.
    Anwar HO (1965) Flow in a free vortex. Water Power 4:106–116Google Scholar
  4. 4.
    Odgaard AJ (1986) Free-surface air core vortex. J Hydraul Eng ASCE 112:610–620CrossRefGoogle Scholar
  5. 5.
    Yildirim N, Kocabas F (1995) Critical submergence for intakes in open-channel flow. J Hydraul Eng ASCE 121:900–905CrossRefGoogle Scholar
  6. 6.
    Yildirim N, Kocabas F (1998) Critical submergence for intakes in still-water reservoir. J Hydraul Eng ASCE 124:103–104CrossRefGoogle Scholar
  7. 7.
    Yildirim N, Kocabas F, Gulcan SC (2000) Flow-boundary effects on critical submergence of intake pipe. J Hydraul Eng ASCE 126:288–297CrossRefGoogle Scholar
  8. 8.
    Yildirim N, Kocabas F (2002) Prediction of critical submergence for an intake pipe. J Hydraul Res 40:507–518CrossRefGoogle Scholar
  9. 9.
    Yildirim N (2004) Critical submergence for a rectangular intake. J Eng Mech ASCE 130:1195–1210CrossRefGoogle Scholar
  10. 10.
    Yildirim N, Tastan K, Arslan MM (2009) Critical submergence for dual pipe intakes. J Hydraul Res 47:242–249CrossRefGoogle Scholar
  11. 11.
    Eroglu N, Bahadirli T (2007) Prediction of critical submergence for a rectangular intake. J Energ Eng ASCE 133:91–103CrossRefGoogle Scholar
  12. 12.
    Kocabas F, Unal S, Unal B (2008) A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom. Comput Fluids 37:1040–1046CrossRefMATHGoogle Scholar
  13. 13.
    Kocabas F, Unal S (2010) Compared techniques for the critical submergence of an intake in water flow. Adv Eng Softw 41:802–809CrossRefMATHGoogle Scholar
  14. 14.
    Unal S (2006) Effect of permeability on air entraining of intakes in still water and open channel environment. MSc thesis, Erciyes University, Kayseri, TurkeyGoogle Scholar
  15. 15.
    Russell SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Res Plan ASCE 122:165–170CrossRefGoogle Scholar
  16. 16.
    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 40:438–444CrossRefMATHGoogle Scholar
  17. 17.
    Unal B, Mamak M, Seckin G, Cobaner M (2010) Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Adv Eng Softw 41:120–129CrossRefMATHGoogle Scholar
  18. 18.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:38–53MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kosko B (1993) Fuzzy thinking: the new science of fuzzy logic. Hyperion, New YorkGoogle Scholar
  20. 20.
    Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, Inc, New YorkMATHGoogle Scholar
  21. 21.
    Kisi O (2010) Fuzzy genetic approach for modeling reference evapotranspiration. J Irrig Drain E ASCE 136:175–183CrossRefGoogle Scholar
  22. 22.
    Sen Z (1998) Fuzzy algorithm for estimation of solar irradiation from sunshine duration. Sol Energy 63:39–49CrossRefGoogle Scholar
  23. 23.
    Kiszka JB, Kochanska ME, Sliwinska DS (1985) The influence of some fuzzy implication operators on the accuracy of a fuzzy model. 1. Fuzzy Set Syst 15:111–128MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Kiszka JB, Kochanska ME, Sliwinska DS (1985) The influence of some fuzzy implication operators on the accuracy of a fuzzy model. 2. Fuzzy Set Syst 15:223–240MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Jang JSR (1993) Anfis—adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  26. 26.
    Burn DH, Yulianti JS (2001) Waste-load allocation using genetic algorithms. J Water Res Plan ASCE 127:121–129CrossRefGoogle Scholar
  27. 27.
    Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manag 19:145–161CrossRefGoogle Scholar
  28. 28.
    Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour Res 27:2467–2471CrossRefGoogle Scholar
  29. 29.
    Şen Z (2004) Genetik Algoritmalar ve Eniyileme Yöntemleri Yöntemleri (Genetic algorithms and optimization methods), Su Vakfi (Water Foundation), IstanbulGoogle Scholar
  30. 30.
    Buckless BP, Petry FE (1994) An overview of genetic algorithm and their applications. In: Buckless BP, Petry FE (eds) Genetic algorithms. IEEE Computer Society Press, New JerseyGoogle Scholar
  31. 31.
    Altunkaynak A (2008) Adaptive estimation of wave parameters by Geno-Kalman filtering. Ocean Eng 35:1245–1251CrossRefGoogle Scholar
  32. 32.
    Altunkaynak A (2009) Sediment load prediction by genetic algorithms. Adv Eng Softw 40:928–934CrossRefMATHGoogle Scholar
  33. 33.
    Bilhan O, Emiroglu ME, Kisi O (2010) Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel. Adv Eng Softw 41:831–837CrossRefMATHGoogle Scholar
  34. 34.
    Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civil Eng ASCE 8:201–220CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Fikret Kocabaş
    • 1
  • Burhan Ünal
    • 2
  • Serap Ünal
    • 3
  • Halil İbrahim Fedakar
    • 4
  • Ercan Gemici
    • 1
  1. 1.Hydraulics Division, Civil Engineering Department, Faculty of EngineeringBartin UniversityBartinTurkey
  2. 2.Hydraulics Division, Civil Engineering Department, Faculty of Engineering and ArchitectureBozok UniversityYozgatTurkey
  3. 3.State Hydraulic WorksYozgatTurkey
  4. 4.Civil Engineering Department, Faculty of EngineeringGaziantep UniversityGaziantepTurkey

Personalised recommendations