Skip to main content
Erschienen in: Neural Computing and Applications 9/2018

15.02.2017 | Original Article

New radial basis function network method based on decision trees to predict flow variables in a curved channel

verfasst von: Azadeh Gholami, Hossein Bonakdari, Amir Hossein Zaji, Salma Ajeel Fenjan, Ali Akbar Akhtari

Erschienen in: Neural Computing and Applications | Ausgabe 9/2018

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Open channel bends have fascinated engineers and scientists for decades while providing water for domestic, irrigation and industrial consumption. The presence of curvature in a channel impacts the flow pattern, velocity and water surface profile. Simulating flow variables such as velocity and water surface depth is one of the most important matters in the design and application of open channel bends. This study investigates a new neural network method using the radial basis function (RBF) based on decision trees (DT-RBF) to predict velocity and free-surface water profiles in a 90° open channel bend. In this study, 506 flow depth and 520 depth-averaged velocity field data obtained at 5 different discharges (5, 7.8, 13.6, 19.1 and 25.3 l/s) in a 90° sharp bend were used for training and testing purposes. The obtained results showed that the proposed DT-RBF models were more accurate than RBF models in estimating flow depth and depth-averaged velocity in the bend. The RBF root-mean-square error (RMSE), mean absolute error (MAE) and relative error (δ) were reduced by 20, 24 and 23.5%, respectively, when using the hybrid DT-RBF model to estimate the depth-averaged velocity. For water surface prediction, the RMSE, MAE and δ decreased by 33, 27.5 and 37%, respectively, when using the proposed DT-RBF hybrid model. For the longitudinal profiles of water surface profile prediction at the outer edge, MAE (0.018) improved to MAE (0.0084) with DT-RBF. It was found that the hybrid decision tree-based method significantly improved RBF neural network performance in forecasting the velocity and free-surface water profiles in a 90° open channel sharp bend.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Kimura I, Takimoto S, Blanckaert K, Shimizu Y, Hosoda T (2010) 3D RANS computations of open channel flows with a sharp bend. In: Proceedings of the 6th international symposium on environmental hydraulics, Athens, Greece, 23–25 June 2010, pp 961–966 Kimura I, Takimoto S, Blanckaert K, Shimizu Y, Hosoda T (2010) 3D RANS computations of open channel flows with a sharp bend. In: Proceedings of the 6th international symposium on environmental hydraulics, Athens, Greece, 23–25 June 2010, pp 961–966
2.
Zurück zum Zitat Shukry A (1950) Flow around bends in an open flume. Trans ASCE 115:751–788 Shukry A (1950) Flow around bends in an open flume. Trans ASCE 115:751–788
3.
Zurück zum Zitat Rozovskii IL (1961) Flow of water in bends of open channels. Academy of Sciences of the Ukrainian SSR, Israel Program for Science Translation, Jerusalem, pp 1–233 Rozovskii IL (1961) Flow of water in bends of open channels. Academy of Sciences of the Ukrainian SSR, Israel Program for Science Translation, Jerusalem, pp 1–233
4.
Zurück zum Zitat DeVriend HJ, Geoldof HJ (1983) Main flow velocity in short river bends. J Hydraul Eng 109(7):991–1011CrossRef DeVriend HJ, Geoldof HJ (1983) Main flow velocity in short river bends. J Hydraul Eng 109(7):991–1011CrossRef
5.
Zurück zum Zitat Steffler PM, Rajartnam N, Peterson AW (1985) Water surface change of channel curvature. J Hydraul Eng 111(5):866–870CrossRef Steffler PM, Rajartnam N, Peterson AW (1985) Water surface change of channel curvature. J Hydraul Eng 111(5):866–870CrossRef
6.
Zurück zum Zitat Ye J, McCorquodale JA (1998) Simulation of curved open channel flows by 3D hydrodynamic model. J Hydraul Eng 124(7):687–698CrossRef Ye J, McCorquodale JA (1998) Simulation of curved open channel flows by 3D hydrodynamic model. J Hydraul Eng 124(7):687–698CrossRef
7.
8.
Zurück zum Zitat Uddin MN, Rahman MM (2012) Flow and erosion at a bend in the braided Jamuna River. Int J Sediment Res 27(4):498–509CrossRef Uddin MN, Rahman MM (2012) Flow and erosion at a bend in the braided Jamuna River. Int J Sediment Res 27(4):498–509CrossRef
9.
Zurück zum Zitat Barbhuiya AK, Talukdar S (2010) Scour and three dimensional turbulent flow fields measured by ADV at a 90 degree horizontal forced bend in a rectangular channel. Flow Meas Instrum 21(3):312–321CrossRef Barbhuiya AK, Talukdar S (2010) Scour and three dimensional turbulent flow fields measured by ADV at a 90 degree horizontal forced bend in a rectangular channel. Flow Meas Instrum 21(3):312–321CrossRef
10.
Zurück zum Zitat Naji MA, Ghodsian M, Vaghefi M, Panahpur N (2010) Experimental and numerical simulation of flow in a 90° bend. Flow Meas Instrum 21(3):292–298CrossRef Naji MA, Ghodsian M, Vaghefi M, Panahpur N (2010) Experimental and numerical simulation of flow in a 90° bend. Flow Meas Instrum 21(3):292–298CrossRef
11.
Zurück zum Zitat Akhtari AA, Abrishami J, Sharifi MB (2009) Experimental investigations water surface characteristics in strongly-curved open channels. J Appl Sci 9(20):3699–3706CrossRef Akhtari AA, Abrishami J, Sharifi MB (2009) Experimental investigations water surface characteristics in strongly-curved open channels. J Appl Sci 9(20):3699–3706CrossRef
12.
Zurück zum Zitat Ramamurthy AS, Han S, Biron PM (2013) Three-dimensional simulation parameters for 90° open channel bend flows. J Comput Civil Eng 27(3):282–291CrossRef Ramamurthy AS, Han S, Biron PM (2013) Three-dimensional simulation parameters for 90° open channel bend flows. J Comput Civil Eng 27(3):282–291CrossRef
13.
Zurück zum Zitat Gholami A, Akhtari AA, Minatour Y, Bonakdari H, Javadi AA (2014) Experimental and numerical study on velocity fields and water surface profile in a strongly-curved 90° open channel bend. Eng Appl Comput Fluid Mech 8(3):447–461 Gholami A, Akhtari AA, Minatour Y, Bonakdari H, Javadi AA (2014) Experimental and numerical study on velocity fields and water surface profile in a strongly-curved 90° open channel bend. Eng Appl Comput Fluid Mech 8(3):447–461
14.
Zurück zum Zitat Vaghefi M, Akbari M, Fiouz AR (2015) Experimental investigation of the three-dimensional flow velocity components in a 180 degree sharp bend. World Appl Progr 5(9):125–131 Vaghefi M, Akbari M, Fiouz AR (2015) Experimental investigation of the three-dimensional flow velocity components in a 180 degree sharp bend. World Appl Progr 5(9):125–131
15.
Zurück zum Zitat Ghobadian R, Mohammadi K (2011) Simulation of subcritical flow pattern in 180° uniform and convergent open-channel bends using SSIIM3-D model. Water Sci Eng 4(3):270–283 Ghobadian R, Mohammadi K (2011) Simulation of subcritical flow pattern in 180° uniform and convergent open-channel bends using SSIIM3-D model. Water Sci Eng 4(3):270–283
16.
Zurück zum Zitat Vaghefi M, Ghodsian M, Neyshabouri SAAS (2012) Experimental study on scour around a T-shaped spur dike in a channel bend. J Hydraul Eng 138:471–474CrossRef Vaghefi M, Ghodsian M, Neyshabouri SAAS (2012) Experimental study on scour around a T-shaped spur dike in a channel bend. J Hydraul Eng 138:471–474CrossRef
17.
Zurück zum Zitat Han S, Ramamurthy AS, Biron PM (2011) Characteristics of flow around open channel 90° bends with vanes. J Irrig Drain Eng 137(10):668–676CrossRef Han S, Ramamurthy AS, Biron PM (2011) Characteristics of flow around open channel 90° bends with vanes. J Irrig Drain Eng 137(10):668–676CrossRef
18.
Zurück zum Zitat Han S, Biron PM, Ramamurthy AS (2011) Three-dimensional modelling of flow in sharp open-channel bends with vanes. J Hydraul Eng 49(1):64–72CrossRef Han S, Biron PM, Ramamurthy AS (2011) Three-dimensional modelling of flow in sharp open-channel bends with vanes. J Hydraul Eng 49(1):64–72CrossRef
19.
Zurück zum Zitat Beygipoor Gh, Bajestan MS, Kaskuli HA, Nazari S (2013) The effects of submerged vane angle on sediment entry to an intake from a 90 degree converged bend. Adv Environ Biol 7(9):2283–2292 Beygipoor Gh, Bajestan MS, Kaskuli HA, Nazari S (2013) The effects of submerged vane angle on sediment entry to an intake from a 90 degree converged bend. Adv Environ Biol 7(9):2283–2292
20.
Zurück zum Zitat Tayfur G (2002) Artificial neural network for sheet sediment transport. Hydrol Sci J 47(6):879–892CrossRef Tayfur G (2002) Artificial neural network for sheet sediment transport. Hydrol Sci J 47(6):879–892CrossRef
21.
Zurück zum Zitat Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63CrossRef Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63CrossRef
22.
Zurück zum Zitat Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(1–2):199–212CrossRef Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(1–2):199–212CrossRef
23.
Zurück zum Zitat Zeng Y, Huai W (2009) Application of artificial neural network to predict the friction factor of open channel flow. Commun Nonlinear Sci Numer Simul 14:2373–2378CrossRef Zeng Y, Huai W (2009) Application of artificial neural network to predict the friction factor of open channel flow. Commun Nonlinear Sci Numer Simul 14:2373–2378CrossRef
24.
Zurück zum Zitat Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36(5):620–627CrossRef Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36(5):620–627CrossRef
25.
Zurück zum Zitat Riahi HM, Ayyoubzadeh SA, Atani MG (2011) Developing an expert system for predicting alluvial channel geometry using ANN. Expert Sys Appl 38(1):215–222CrossRef Riahi HM, Ayyoubzadeh SA, Atani MG (2011) Developing an expert system for predicting alluvial channel geometry using ANN. Expert Sys Appl 38(1):215–222CrossRef
26.
Zurück zum Zitat Akbari M, Solaimani K, Mahdavi M, Habibnejhad M (2011) Monitoring of regional low-flow frequency using artificial neural networks. J Water Sci Res 3(1):1–17 Akbari M, Solaimani K, Mahdavi M, Habibnejhad M (2011) Monitoring of regional low-flow frequency using artificial neural networks. J Water Sci Res 3(1):1–17
27.
Zurück zum Zitat Zaji AH, Bonakdari H (2014) Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Meas Instrum 40:149–156CrossRef Zaji AH, Bonakdari H (2014) Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Meas Instrum 40:149–156CrossRef
28.
Zurück zum Zitat Zaji AH, Bonakdari H (2015) Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions. Flow Meas Instrum 41:81–89CrossRef Zaji AH, Bonakdari H (2015) Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions. Flow Meas Instrum 41:81–89CrossRef
29.
Zurück zum Zitat Petković D, Gocic M, Trajkovic S, Shamshirband S, Pavlović NT, Bonakdari H (2015) Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Comput Electron Agric 114:277–284CrossRef Petković D, Gocic M, Trajkovic S, Shamshirband S, Pavlović NT, Bonakdari H (2015) Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Comput Electron Agric 114:277–284CrossRef
30.
31.
Zurück zum Zitat Tahershamsi A, Menhaj MB, Ahmadian R (2006) Sediment loads prediction using multilayer feedforward neural networks. Amirkabir J Sci Technol 16(63):103–110 Tahershamsi A, Menhaj MB, Ahmadian R (2006) Sediment loads prediction using multilayer feedforward neural networks. Amirkabir J Sci Technol 16(63):103–110
32.
Zurück zum Zitat Kumar B, Sreenivasulu G, Ramakrishna Rao A (2010) Radial basis function network based design of alluvial channels with seepage. J Hydrol Hydromech 58(2):102–113CrossRef Kumar B, Sreenivasulu G, Ramakrishna Rao A (2010) Radial basis function network based design of alluvial channels with seepage. J Hydrol Hydromech 58(2):102–113CrossRef
33.
Zurück zum Zitat Tahershamsi A, Majdzade Tabatabai MR, Shirkhani R (2012) An evaluation model of artificial neural network to predict stable width in gravel bed rivers. Int J Environ Sci Technol 9:333–342CrossRef Tahershamsi A, Majdzade Tabatabai MR, Shirkhani R (2012) An evaluation model of artificial neural network to predict stable width in gravel bed rivers. Int J Environ Sci Technol 9:333–342CrossRef
34.
Zurück zum Zitat Senthil Kumar AR, Ojha CSP, Manish Kumar G, Singh RD, Swamee PK (2012) Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms. J Hydrol Eng 17(3):394–404CrossRef Senthil Kumar AR, Ojha CSP, Manish Kumar G, Singh RD, Swamee PK (2012) Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms. J Hydrol Eng 17(3):394–404CrossRef
35.
Zurück zum Zitat Bonakdari H, Baghalian S, Nazari F, Fazli M (2011) Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Eng Appl Comput Fluid Mech 5(3):384–396 Bonakdari H, Baghalian S, Nazari F, Fazli M (2011) Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Eng Appl Comput Fluid Mech 5(3):384–396
36.
Zurück zum Zitat Baghalian S, Bonakdari H, Nazari F, Fazli M (2012) Closed-form solution for flow field in curved channel in comparison with experimental and numerical analysis and artificial neural network. Eng Appl Comput Fluid Mech 6(4):514–526 Baghalian S, Bonakdari H, Nazari F, Fazli M (2012) Closed-form solution for flow field in curved channel in comparison with experimental and numerical analysis and artificial neural network. Eng Appl Comput Fluid Mech 6(4):514–526
37.
Zurück zum Zitat Sahu M, Jana S, Agarwal S, Khatua KK (2011) Point form velocity prediction in meandering open channel using artificial neural network. In: 2nd International conference on environmental science and technology, vol 6. IACSIT Press, Singapore, pp 209–212 Sahu M, Jana S, Agarwal S, Khatua KK (2011) Point form velocity prediction in meandering open channel using artificial neural network. In: 2nd International conference on environmental science and technology, vol 6. IACSIT Press, Singapore, pp 209–212
38.
Zurück zum Zitat Gholami A, Bonakdari H, Zaji AH, Akhtari AA (2015) Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks. Eng Appl Comput Fluid Mech 9(1):355–369 Gholami A, Bonakdari H, Zaji AH, Akhtari AA (2015) Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks. Eng Appl Comput Fluid Mech 9(1):355–369
40.
Zurück zum Zitat Chen W, Fu ZJ, Chen CS (2014) Recent advances in radial basis function collocation methods. Springer, HeidelbergCrossRef Chen W, Fu ZJ, Chen CS (2014) Recent advances in radial basis function collocation methods. Springer, HeidelbergCrossRef
41.
Zurück zum Zitat Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:2449–2460CrossRef Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:2449–2460CrossRef
42.
Zurück zum Zitat Coppersmith D, Hong SJ, Hosking JRM (1999) Partitioning nominal attributes in decision trees. Data Min Knowl Disc 3(2):197–217CrossRef Coppersmith D, Hong SJ, Hosking JRM (1999) Partitioning nominal attributes in decision trees. Data Min Knowl Disc 3(2):197–217CrossRef
Metadaten
Titel
New radial basis function network method based on decision trees to predict flow variables in a curved channel
verfasst von
Azadeh Gholami
Hossein Bonakdari
Amir Hossein Zaji
Salma Ajeel Fenjan
Ali Akbar Akhtari
Publikationsdatum
15.02.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2875-1

Weitere Artikel der Ausgabe 9/2018

Neural Computing and Applications 9/2018 Zur Ausgabe