Estimating soil wetting patterns for drip irrigation using genetic programming

  • S. Samadianfard Water Engineering Department, Agricultural Faculty, University of Tabriz, Iran
  • A. A. Sadraddini Water Engineering Department, Agricultural Faculty, University of Tabriz, Iran
  • A. H. Nazemi Water Engineering Department, Agricultural Faculty, University of Tabriz, Iran
  • G. Provenzano Dip. I.T.A.F. Sezione Idraulica. Univ. degli Studi di Palermo, Viale elle Scienze 12, 90128 Palermo, Italy
  • O. Kisi Architecture and Engineering Faculty, Civil Engineering Department, Canik Basari University, Samsun, Turkey
Keywords: genetic programming, HYDRUS 2D, infiltration, numerical models, soil texture triangle

Abstract

Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA–SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R2 of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h-1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation.

Downloads

Download data is not yet available.

References



Abbasi F, Simunek J, Feyen J, Van Genuchten MTh, Shouse PJ, 2003a. Simultaneous inverse estimation of soil hydraulic and solute transport parameters from transient field experiments: Homogeneous soil. T ASAE 46(4): 1085-1095. 

Abbasi F, Jacques D, Simunek J, Feyen J, Van Genuchten MTh, 2003b. Inverse estimation of the soil hydraulic and solute transport parameters from transient field experiments: Heterogeneous soil. T ASAE 46(4): 1097-1111. 

Assouline S, 2002. The effects of micro drip and conventional drip irrigation on water distribution and uptake. Soil Sci Soc Am J 66: 1630-1636.
http://dx.doi.org/10.2136/sssaj2002.1630 

Aytac A, Alp M, 2008. An application of artificial intelligence for rainfall-runoff modeling. J Earth Syst Sci 117(2): 144-155. 

Ben-Asher J, Charach C, Zemel A, 1986. Infiltration and water extraction from trickle-irrigation source. The effective hemisphere model. Soil Sci Soc Am J 50: 882-887.
http://dx.doi.org/10.2136/sssaj1986.03615995005000040010x 

Brandt A, Breslker E, Diner N, Ben-Asher J, Heller J, Goldberg D, 1971. Infiltration from a trickle source: I. Mathematical models. Soil Sci Soc Am J 35: 683-689.
http://dx.doi.org/10.2136/sssaj1971.03615995003500050018x 

Bresler E, 1978. Analysis of trickle irrigation with application to design problems. Irrigation Sci 1: 3-17.
http://dx.doi.org/10.1007/BF00269003 

Bufon VB, Lascano RJ, Bednarz C, Booker JD, Gitz DC, 2012. Soil water content on drip irrigated cotton: comparison of measured and simulated values obtained with the Hydrus 2-D model. Irrigation Sci 30: 259-273.
http://dx.doi.org/10.1007/s00271-011-0279-z 

Camp CR, 1998. Subsurface drip irrigation: a review. T ASAE 41: 1353-1367. 

Cote CM, Bristow KL, Charlesworth PB, Cook FJ, Thorburn PJ, 2003. Analysis of soil wetting and solute transport in subsurface trickle irrigation. Irrigation Sci 22: 143-156.
http://dx.doi.org/10.1007/s00271-003-0080-8 

Darusman KAH, Stone LR, Lamm FR, 1997. Water flux below the root zone vs. drip-line spacing in drip irrigated corn. Soil Sci Soc Am Proc 61: 1755-1760. 

Ferreira C, 2001a. Gene expression programming in problem solving. 6th Online World Conf on Soft Computing in Industrial Applications (invited tutorial), Springer, Berlin (Germany). pp: 1-22.

 

Ferreira C, 2001b. Gene expression programming: A new adaptive algorithm for solving problems. Complex Syst 13(2): 87-129. 

Ferreira C, 2006. Gene expression programming: mathematical modeling by an artificial intelligence. Springer, Berlin, 478 pp. 

Fuchs M, 1998. Crossover versus mutation: An empirical and theoretical case study. Proc. 3rd Ann Conf on Genetic Programming, Morgan- Kauffman, San Mateo, CA, (USA), Jul 22-25. pp: 78-85.

 

Goldberg DE, 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA, USA. 

Healy RW, 1987. Simulation of trickle-irrigation, an extension to U.S. Geological Survey's computer program Vs 2D. US Geological Survey Water Resour Invest 87-4086, US Govt Washington, DC. 

Hinnell AC, Lazarovitch N, Furman A, Poulton M, Warrick AW, 2010. Neuro-Drip: estimation of subsurface wetting patterns for drip irrigation using neural networks. Irrigation Sci 28: 535-544.
http://dx.doi.org/10.1007/s00271-010-0214-8 

Kandelous MM, Simunek J, 2010. Comparison of numerical, analytical, and empirical models to estimate wetting patterns for surface and subsurface drip irrigation. Irrigation Sci 28: 435-444.
http://dx.doi.org/10.1007/s00271-009-0205-9 

Kisi O, Guven A, 2010. Evapotranspiration modeling using linear genetic programming technique. J Irrig Drain Eng-ASCE 136(10): 715-723.
http://dx.doi.org/10.1061/(ASCE)IR.1943-4774.0000244 

Koza JR, 1992. Genetic programming, on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA.
PMCid:1130644 

Kumar DN, Raju KS, Ashok B, 2006. Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. J Irrig Drain Eng-ASCE 132(2): 123-129.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2006)132:2(123) 

Lubana PPS, Narda NK, 2001. Soil and water modelling. Soil water dynamics under trickle emitters-a review. J Agr Eng Res 78: 217-232.
http://dx.doi.org/10.1006/jaer.2000.0650 

Luke S, Spector L, 1998. A revised comparison of crossover and mutation in genetic programming. Proc 3rd Ann Conf on Genetic Programming, Morgan-Kauffman, Madison, San Mateo, CA, USA. 

Mmolawa K, Or D, 2000a. Water and solute dynamics under a drip irrigated crop: experiments and analytical model. T ASAE 43: 1597-1608. 

Mmolawa K, Or D, 2000b. Root zone solute dynamics under drip irrigation: A review. Plant Soil 222: 163-190.
http://dx.doi.org/10.1023/A:1004756832038 

Moradi-Jalal M, Rodin SI, Marino MA, 2004. Use of genetic algorithm in optimization of irrigation pumping stations. J Irrig Drain Eng-ASCE 130(5): 357-365.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2004)130:5(357) 

Mualem Y, 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour Res 12(3): 513-522.
http://dx.doi.org/10.1029/WR012i003p00513 

Or D, 1995. Statistical analysis of soil water monitoring for drip irrigation management in heterogeneous soils. Soil Sci Soc Am J 59: 1222-1233.
http://dx.doi.org/10.2136/sssaj1995.03615995005900050003x 

Reca J, Martinez J, 2006. Genetic algorithms for the design of looped irrigation water distribution networks. Water Resour Res 42(5): 1-9.
http://dx.doi.org/10.1029/2005WR004383 

Simunek J, Van Genuchten MT, Senja M, 2006. The HYDRUS software package for simulating two-and three-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Technical manual, Vers 1.0. PC Progress, Prague, Czech Republic.
PMCid:2014688 

Skaggs TH, Trout TJ, Simunek J, Shouse PJ, 2004. Comparison of Hydrus-2D simulations of drip irrigation with experimental observations. J Irrig Drain Eng-ASCE 130(4): 304-310.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2004)130:4(304) 

Smith RE, Warrick AW, 2007. Soil water relationships- design and operation of farm irrigation systems. American Society of Agricultural and Biological Engineers (ASABE), Ann Arbor (USA), pp: 120-159.
PMCid:2690923 

Steele DD, Greenland RG, Gregor BL, 1996. Subsurface drip irrigation systems for specialty crop production in North Dakota. Appl Eng Agr 12(6): 671-679. 

Taghavi SA, Marino Miguel A, Rolston DE, 1984. Infiltration from trickle-irrigation source. J Irrig Drain Eng-ASCE 10: 331-341.
http://dx.doi.org/10.1061/(ASCE)0733-9437(1984)110:4(331) 

Ustoorikar K, Deo MC, 2008. Filling up gaps in wave data with genetic programming. Mar Struct 21: 177-195.
http://dx.doi.org/10.1016/j.marstruc.2007.12.001 

Van Genuchten MTh, 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44: 892-898.
http://dx.doi.org/10.2136/sssaj1980.03615995004400050002x 

Vrugt JA, Van Wijk MT, Hopmans JW, Simunek J, 2001. One-, two-, three dimensional root water uptake functions for transient modeling. Water Resour Res 37(10): 2457-2470.
http://dx.doi.org/10.1029/2000WR000027 

Warrick AW, 1974. Time-dependent linearized infiltration. I. Point sources. Soil Sci Soc Am Proc 38: 383-386.
http://dx.doi.org/10.2136/sssaj1974.03615995003800030008x 

Wooding RA, 1968. Steady infiltration from a shallow circular pond. Water Resour Res 4: 1259-1273.
http://dx.doi.org/10.1029/WR004i006p01259 

Published
2012-11-08
How to Cite
Samadianfard, S., Sadraddini, A. A., Nazemi, A. H., Provenzano, G., & Kisi, O. (2012). Estimating soil wetting patterns for drip irrigation using genetic programming. Spanish Journal of Agricultural Research, 10(4), 1155-1166. https://doi.org/10.5424/sjar/2012104-502-11
Section
Water management