Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters

Computers and Electronics in Agriculture

Volumn 114, Issue , 2015, Pages 189-201

  Mattar, Mohamed A ^a,b   Alamoud, Ahmed I ^a  

^a KING SAUD UNIVERSITY   (Saudi Arabia)

^b Agricultural Research Center   (Egypt)

Author keywords

Artificial neural network; Emitter flow variation; Labyrinth emitter; Manufacturer's coefficient of variation

Indexed keywords

BACKPROPAGATION; ERRORS; HYPERBOLIC FUNCTIONS; LINEAR REGRESSION; MANUFACTURE; NEURAL NETWORKS; STATISTICAL TESTS; SUPPLY CHAINS; VALUE ENGINEERING;

COEFFICIENT OF VARIATION; FEED-FORWARD BACK PROPAGATION NETWORKS; FLOW VARIATIONS; INDEPENDENT VARIABLES; LABYRINTH EMITTER; MULTIPLE LINEAR REGRESSION MODELS; ROOT MEAN SQUARE ERRORS; STATISTICAL CRITERION;

MEAN SQUARE ERROR;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; DATA SET; HYDRAULIC CONDUCTIVITY; MANUFACTURING; NUMERICAL MODEL; OPERATIONS TECHNOLOGY; PRESSURE EFFECT; STATISTICAL ANALYSIS; WATER TEMPERATURE;

EID: 84928169354     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.04.007     Document Type: Article

References (48)

1. Alamoud A.I., Mattar M.A., Ateia M. Impact of water temperature and structural parameters on the hydraulic labyrinth-channel emitter performance. Span. J. Agric. Res. 2014, 12(2):580-593.
2. Alazba A.A., Mattar M.A., ElNesr M.N., Amin M.T. Field assessment of friction head loss and friction correction factor equations. J. Irrig. Drain. Eng., ASCE 2012, 138(2):166-176.
3. ASABE, 2005. EP-458: Field evaluation of microirrigation systems. ASABE, St. Joseph, USA.
4. ASCE Task committee Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng., ASCE 2000, 5(2):115-123.
5. Bralts V.F., Wu I.P., Gitlin H.M. Manufacturing variation and drip irrigation uniformity. Trans. ASABE 1981, 24(1):113-119.
6. Cigizoglu H.K. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol. Sci. J. 2003, 48(3):349-361.
7. Cobaner M., Citakoglu H., Kişi Ö., Haktanir T. Estimation of mean monthly air temperatures in Turkey. Comput. Electron. Agric. 2014, 109(8):71-79.
8. Dawson W.C., Wilby R. An artificial neural networks approach to rainfall-runoff modeling. Hydrol. Sci. J. 1998, 43(1):47-66.
9. Evans R.G., Wu I., Smajstrala A.G. Microirrigation systems. Design and Operation of Irrigation Systems 2007, 632-683. American Society of Agricultural and Biological Engineers Special Monograph, (Chapter 17). second ed.
10. Fernando D.A.K., Shamseldin A.Y. Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models. J. Hydrol. Eng., ASCE 2009, 14(3):286-292.
11. Givi J., Prasher S.O., Patel R.M. Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point. Agric. Water Manage. 2004, 70(2):83-96.
12. Gocić M., Motamedi S., Shamshirband S., Petković D., Ch S., Hashim R., Arif M. Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric. 2015, 113(2):164-173.
13. Haykin S. Neural Networks. A Comprehensive Foundation 1999, Prentice Hall International Inc., New Jersey.
14. Hinnell A.C., Lazarovitch N., Furman A., Poulton M., Warrick A.W. Neuro-Drip: estimation of subsurface wetting patterns for drip irrigation using neural networks. Irri. Sci. 2009, 28(6):535-544.
15. Jain A., Srinivasulu S. Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neural network techniques. Water Resour. Res. 2004, 40(4):W04302.
16. Kang S., Yulong Z., Zhuangde J. Laser profilometer. Opt. Precis. Eng. 2003, 11(3):245-248.
17. Kumar S., Singh P. Evaluation of hydraulic performance of drip irrigation system. J. Agric. Eng. 2007, 44(2):104-108.
18. Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W., Pruitt W.O. Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng., ASCE 2002, 128(4):224-233.
19. Landeras G., Ortiz-Barredo A., López J.J. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country Northern Spain. Agric. Water Manage. 2008, 95(5):553-565.
20. Landeras G., Ortiz-Barredo A., López J.J. Forecasting weekly evapotranspiration with ARIMA and artificial neural network Models. J. Irrig. Drain. Eng., ASCE 2009, 135(3):323-334.
21. Legates D.R., McCabe G.J. Evaluating the use of ''goodness-of fit'' measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35(1):233-241.
22. Madramootoo C.A., Khatri K.C., Rigby M. Hydraulic performances of five different trickle irrigation emitters. Can. Agric. Eng. 1988, 30:1-4.
23. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and application. Environ. Model. Software 2000, 15(1):101-124.
24. Martí P., Provenzano G., Royuela Á., Palau-Salvador G. Integrated emitter local loss prediction using artificial neural networks. J. Irrig. Drain. Eng., ASCE 2010, 136(1):11-22.
25. Mattar M.A., Alazba A.A., Zin El-Abedin T.K. Forecasting furrow irrigation infiltration using artificial neural networks. Agric. Water Manage. 2015, 148(1):63-71.
26. Mizyed N., Kruse E.G. Emitter discharge evaluation of subsurface trickle irrigation. Trans. ASABE 1989, 32(4):1223-1228.
27. Montgomery D., Runger G. Applied Statistics and Probability for Engineers 2003, John Wiley & Sons Inc., United States of America.
28. Montgomery D., Peck E., Vining G. Introduction to linear regression analysis 2001, John Wiley, New York. third ed.
29. Nakayama F.S., Bucks D.A. Trickle Irrigation for Crop Production 1986, Elsevier Science, Amsterdam, pp. 1-383.
30. Özekici B., Bozkurt S. Determination of hydraulic performances of in-line emitters. Turk. J. Agric. Forest. 1999, 23(1):19-24.
31. Özekici B., Sneed R.E. Manufacturing variation for various trickle irrigation on-line emitter. Appl. Eng. Agric. 1995, 11(2):235-240.
32. Raju S.K., Kumar D.N., Duck L. Artificial neural networks and multicriterion analysis for sustainable irrigation planning. Comput. Oper. Res. 2006, 33:1138-1153.
33. Rodriguez-Sinobas L., Juana L., Losada A. Effects of temperature changes on emitter discharge. J. Irrig. Drain. Eng., ASCE 1999, 125(2):64-73.
34. Singh A.K., Upadhyaya A., Islam A., Bharali M.A., Roy M. Flow and manufacturing variation of drippers. J. Agric. Eng. 2006, 43(3):27-30.
35. Singh D.K., Rajput T.B.S., Singh D.K., Sikarwar H.S., Sahoo R.N., Ahmad T. Performance of subsurface drip irrigation system with line source of water application in okra field. J. Agric. Eng. 2006, 43(3):23-26.
36. Swingler K. Applying Neural Networks, A Practical Guide 2001, Academic Press, San Francisco, CA. third ed.
37. Thirumalaiah K., Deo M.C. River stage forecasting using artificial neural networks. J. Hydrol. Eng., ASCE 1998, 3(1):26-32.
38. Tripathi V.K., Rajput T.B.S., Patel N., Lata Hydraulic performance of drip irrigation system with municipal wastewater. J. Agric. Eng. 2011, 48(2):15-22.
39. Wanakule, N., Aly, A., 2005. Using groundwater artificial neural network models for adaptive water supply management. In: Proc., Impacts of Global Climate Change, ASCE, Reston, Va., 173(40792), 89.
40. Wei Z., Tang Y., Zhao W., Lu B. Rapid structural design of drip irrigation emitters based on RP technology. Rapid. Prototyping J. 2007, 13(5):268-275.
41. Wu I.P., Gitlin H.M. Drip irrigation application efficiency and schedules. Trans. ASABE 1983, 28:92-99.
42. Zanetti S.S., Sousa E.F., Oliveira V.P.S. Estimating evapotranspiration using artificial neural network and minimum climatological data. J. Irrig. Drain. Eng., ASCE 2007, 133(2):83-89.
43. Zapata N., Nerilli E., Martinez-Cob A., Chalghaf I., Chalghaf B., Fliman D., Playan E. Limitations to adopting regulated deficit irrigation in stone fruit orchards: a case study. Span. J. Agric. Res. 2013, 11(2):529-546.
44. Zhang J., Zhao W., Wei Z., Tang Y., Lu B. Numerical and experimental study on hydraulic performance of emitters with arc labyrinth channels. Comput. Electron. Agric. 2007, 56(2):120-129.
45. Zhang J., Zhao W., Tang Y., Lu B. Structural optimization of labyrinth-channel emitters based on hydraulic and anti-clogging performances. Irri. Sci. 2011, 29(5):351-357.
46. Zhang J., Zhao W., Lu B. New method of hydraulic performance evaluation on emitters with labyrinth channels. J. Irrig. Drain. Eng., ASCE 2011, 137(12):811-815.
47. Zhao W., Zhang J., Tang Y., Wei Z., Lu B. IFIP International Federation for Information Processing. Computer and Computing Technologies in Agriculture II 2009, vol. 2:881-890. Springer, Boston. D. Li, Z. Chunjiang (Eds.).
48. Zhiqin L., Lin L. The influence of the sectional form of labyrinth emitter on the hydraulic properties. AICI 2011, CCIS 237 2011, 499-505. Springer-Verlag Berlin Heidelberg. H. Deng (Ed.).