Modeling energy dissipation over stepped spillways using machine learning approaches

Journal of Hydrology

Volumn 508, Issue , 2014, Pages 254-265

  Roushangar, Kiyoumars ^a   Akhgar, Samira ^a   Salmasi, Farzin ^a   Shiri, Jalal ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

Author keywords

Energy dissipation; Genetic programming; Neural network; Sensitivity analysis

Indexed keywords

FLOOD CONTROL; GENETIC PROGRAMMING; LEARNING SYSTEMS; NEURAL NETWORKS; SENSITIVITY ANALYSIS; SPILLWAYS;

APPLIED MACHINE LEARNING; ARTIFICIAL NETWORKS; GENETIC EXPRESSION PROGRAMMING; INPUT-OUTPUT; MACHINE LEARNING APPROACHES; RELIABLE PERFORMANCE; SKIMMING FLOWS; STEPPED SPILLWAYS;

ENERGY DISSIPATION;

ARTIFICIAL NEURAL NETWORK; DATA SET; ENERGY DISSIPATION; FLOOD CONTROL; FLOW PATTERN; INPUT-OUTPUT ANALYSIS; LEARNING; NAPPE; SENSITIVITY ANALYSIS; SPILLWAY;

EID: 84888409415     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.10.053     Document Type: Article

References (36)

1. Banzhaf W., Nordin P., Keller P.E., Francone F.D. Genetic Programming 1998, Morgan Kaufmann, San Francisco, CA, 512p.
2. Barani G.A., Rahnama M.B., Sohrabipoor N. Investigation of flow energy dissipation over different stepped spillways. American Journal of Applied Sciences 2005, 2(6):1101-1105.
3. Bayat H.O. Stepped spillway model investigation. Proc. 17th ICOLD Congress, Viena, Austria III 1991, 66:1803-1817.
4. Bindo M., Gautier J., Lacroix F. The stepped spillway of M'Bali dam. Journal of Water Power and Dam Construction 1993, 14(1):35-36.
5. Cevik A. A new formulation for longitudinally stiffened webs subjected to patch loading. Journal of Constructional Steel Research 2007, 63(10):1328-1340.
6. Chamani M.R., Rajaratnam N. Jet flow on stepped spillways. ASCE Journal of Hydraulic Engineering 1994, 120(2):254-259.
7. Chanson H. Comparison of energy dissipation in nappe and skimming flow regimes on stepped chutes. Journal of Hydraulic Research 1994, 32(2):213-218.
8. Cheng C.T., Chau K.W., Sun Y., Lin J. Long-term prediction of discharges in Manwan Reservoir using artificial neural network models. Lecture Notes in Computer Science 2005, 3498:1040-1045.
9. Christodoulou G.C. Energy dissipation on stepped spillway. ASCE Journal of Hydraulic Engineering 1993, 119(5):644-649.
10. Diez-Cascon J., Blanco J.L., Revilla J., Garcia R. Studies on the hydraulic behavior of stepped spillway. Journal of International Water Power and Dam Construction 1991, 43(9):22-26.
11. Ferreira C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems 2001, 13(2):87-129.
12. Ferreira C. Gene expression programming: mathematical Modeling by an artificial intelligence 2006, Springer, Berlin, Heidelberg, New York, 478p.
13. Haykin, S., Cybenko, G., 1999. Approximation by Superposition of a Sigmoidal Function Neural Networks, second ed. Prentice Hall, Englewood Cliffs, NJ, Math. Control Signals Syst. 2, pp. 303-314.
14. Kavianpour M.R., Masoumi H.R. New approach for estimating of energy dissipation over stepped spillways. International Journal of Civil Engineering 2008, 6(3):230-237.
15. Kells J.A. Comparison of energy dissipation between nappe and skimming flow regimes on stepped chutes discussion. IAHR Journal of Hydraulic Research 1995, 33(1):128-133.
16. Kim S., Shiri J., Kisi O. Pan evaporation modeling using neural computing approach for different climatic zones. Water Resources Management 2012, 26(11):3231-3249.
17. Kisi O., Shiri J. River suspended sediment estimation by climatic variables implication: comparative study among soft computing techniques. Computers & Geosciences 2012, 43:73-82.
18. Kisi O., Shiri J., Tombul M. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences 2013, 51:108-117.
19. Legates D.R., McCabe G.J. Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic validation. Water Resources Research 1999, 35(1):233-241.
20. Mitchell T.M. Machine Learning 1997, McGraw-Hill, New York.
21. Muñoz, D.G., 2005. Discovering unknown equations that describe large data sets using genetic programming techniques. Master Thesis, Electronic Systems, Linköping Institute of Technology, LITH-ISY-EX-05/3697.
22. Musavi H., Salmasi F. Physical and numerical modeling of the nappe flow in the stepped spillway. Journal of Applied Sciences 2008, 8(9):1720-1725.
23. Naderi Rad I., Teimouri M. An investigation of flow energy dissipation in simple stepped spillways by numerical model. European Journal of Scientific Research 2010, 47(4):544-553.
24. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models, Part 1 - A discussion of principles. Journal of Hydrology 1970, 10(3):282-290.
25. Poli, R., McPhee, N.F., 2008. Covariant parsimony pressure for genetic programming. Technical Report CES-480, ISSN: 1744-8050.
26. Rajaratnam N. Skimming flow in stepped spillways. ASCE Journal of Hydraulic Engineering 1990, 116(4):587-591.
27. Shiri J., Kisi O. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers & Geosciences 2011, 37(10):1692-1701.
28. Shiri J., Kisi O. Estimation of daily suspended sediment load by using wavelet conjunction models. ASCE Journal of Hydrologic Engineering 2012, 17(9):986-1000.
29. Shiri J., Kisi O., Makarynskyy O., Shiri A.A., Nikoofar B. Forecasting daily stream flow using artificial intelligence approaches. ISH Journal of Hydraulic Engineering 2012, 18(3):204-214.
30. Shiri J., Marti P., Singh V.P. Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning. Hydrological Processes 2013, 10.1002/hyp.9669.
31. Sorensen R.M. Stepped spillway hydraulic model investigation. ASCE Journal of Hydraulic Engineering 1985, 111(12):1461-1472.
32. Taormina R. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Engineering Applications of Artificial Intelligence 2012, 25(8):1670-1676.
33. Tozzi M.J. Residual energy in stepped spillways. Water Power and Dam Construction 1994, 32-34.
34. Young, M.F., 1982. Feasibility study of a stepped spillway. In: Proceedings, Hydraulics Division Specialty Conference, ASCE, New York, pp. 96-106.
35. Yuhong Z., Wenxin H. Application of artificial neural network to predict the friction factor of open channel flow. Communications in Nonlinear Science and Numerical Solutions 2009, 14:2373-2378.
36. Zahiri A., Dehghani A.A. Flow discharge determination in straight compound channels using ANNs. World Academy of Science, Engineering and Technology 2009, 58:12-15.