Modeling sprinkler irrigation infiltration based on a fuzzy-logic approach

Mohamed Abdel-Aziz Mattar, Mohamed S. El-Marazky, Khaled A. Ahmed

Abstract


In this study, the irrigation water infiltration rate (IR) is defined by input variables in linguistic terms using a fuzzy-logic approach. A fuzzy-logic model was developed using data collected from published data. The model was trained with three fuzzy membership functions: triangular (‘trimf’), trapezoid (trapmf), and pi (‘pimf’). The fuzzy system considered the number of irrigation events, applied water depth, polyacrylamide application rate, water application time, water electrical conductivity, soil surface slope, and soil texture components as input variables. The inputs were classified in terms of low, medium, and high levels. The output variable (i.e., IR) was rated in terms of five levels: very low, low, medium, high, and very high. Using statistical analysis, the values of IR resulting from the developed fuzzy-logic model were compared with the observations from the experiments. The results confirm that the agreement between the observations and predictive results was acceptable, except for fuzzy 'trimf'. The coefficient of determination provided the greatest value when using the 'trapmf' and 'pimf', with the value estimated for the 'pimf' slightly higher than that of 'trapmf'. Based on the results that were obtained, irrigation managers can use the fuzzy-logic approach to modify their field practices during the growing season to improve on-farm water management.

Keywords


water infiltration; polyacrylamide; sprinkler simulator; artificial intelligence

Full Text:

PDF

References


Abo-Ghobar HM, 1993. Influence of irrigation water quality on soil infiltration. Irrig Sci 14: 15-19. https://doi.org/10.1007/BF00195001

Ajwa H, Trout TJ, 2006. Polyacrylamide and water quality effects on infiltration in sandy loam soils. Soil Sci Soc Am J 70: 643-650. https://doi.org/10.2136/sssaj2005.0079

Alazba AA, Mattar MA, Einesr MN, Amin MT, 2012. Field assessment of friction head loss and friction correction factor equations. J Irrig Drain Eng ASCE 138 (2): 166-176. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000387

Allahverdi N, 2002. Expert systems. An artificial inteligence application. Atlas Press, İstanbul.

Alonso-Garcia S, Gomez-Gil J, Arribas JI, 2011. Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors. Span J Agric Res 9 (2): 377-388. https://doi.org/10.5424/sjar/20110902-088-10

Bjorneberg DL, Aase JK, 2000. Multiple polyacrylamide applications for controlling sprinkler irrigation runoff and erosion. Appl Eng Agric 16: 501-504. https://doi.org/10.13031/2013.5302

Bjorneberg DL, Santos FL, Castanheira NS, Martins OC, Reis JL, Aase JK, Sojka RE, 2003. Using polyacrylamide with sprinkler irrigation to improve infiltration. J Soil Water Conserv 58: 283-289.

Chávez C, Fuentes C, Ventura-Ramos E, 2010. Water use efficience in furrow irrigation with application of gypsum and polyacrylamide (in Spanish). Terra Latinoamericana 28 (3): 231-238.

Clanton CJ, Slack DC, 1987. Hydraulic properties of soils as affected by surface application of wastewater. T ASAE 30: 683-587. https://doi.org/10.13031/2013.30460

Delgado G, Aranda V, Calero J, Sánchez-Marañón M, Serrano JM, Sánchez D, Vila MA, 2008. Building a fuzzy logic information network and a decision-support system for olive cultivation in Andalusia. Span J Agric Res 6 (2): 252-263. https://doi.org/10.5424/sjar/2008062-316

Demirci M, Baltaci A, 2013. Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Comput & Applic 23 (Suppl 1): 145-151. https://doi.org/10.1007/s00521-012-1280-z

Green VS, Stott DE, 2001. Polyacrylamide: Use, effectiveness, and cost of a soil erosion control amendment. In: The Global Farm – Selected papers from the 10th Int Soil Conserv Organiz Meeting; Stott DE, Mohtar R, Steinhardt G (eds.). USDA-ARS Nat Soil Eros Res Lab, May 24-29, 1999, Purdue University, West Lafayette, IN, USA.

Jacquin AP, Shamseldin AY, 2006. Development of rainfall-runoff models using Takagi-Sugeno fuzzy inference systems. J Hydrol 329: 154-173. https://doi.org/10.1016/j.jhydrol.2006.02.009

Jantzen J, 1999. Design of fuzzy controllers. Technical Rep. No. 98-E864, Dept of Automation, Technical Univ of Denmark, Denmark.

Legates DR, McCabe Jr GJ, 1999. Evaluating the use of "goodness-of fit" measures in hydrologic and hydroclimatic model validation. Water Resour Res 35 (1): 233-241. https://doi.org/10.1029/1998WR900018

Leib BG, Redulla CA, Stevens RG, Mattews GR, Strausz DA, 2005. Erosion control practices integrated with polyacrylamide to reduce sediment loss in furrow irrigation. Soil & Water Division of ASAE 1 (4): 595-603.

Lentz RD, Sojka RE, Robbins CW, Kincaid DC, Westermann DT, 2001. Polyacrylamide for surface irrigation to increase nutrient-use efficiency and protect water quality. Comm Soil Sci Plant Anal 32: 1203-1220. https://doi.org/10.1081/CSS-100104109

Mamdani EH, 1974. Applications of fuzzy algorithms for simple dynamic plant. Proc IEE 121 (12): 1585-1588. https://doi.org/10.1049/piee.1974.0328

Mamedov AI, Shainberg I, Levy GJ, 2000. Irrigation with effluent water: effects of rainfall energy on soil infiltration. Soil Sci Soc Am J 64: 732-737. https://doi.org/10.2136/sssaj2000.642732x

Mattar MA, Alamoud AI, 2015. Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters. Comput Electron Agric 114 (5): 189-201. https://doi.org/10.1016/j.compag.2015.04.007

Mattar MA, Alazba AA, Zin El-Abedin TK, 2015. Forecasting furrow irrigation infiltration using artificial neural networks. Agr Water Manag 148 (1): 63-71. https://doi.org/10.1016/j.agwat.2014.09.015

McElhiney M, Osterli P, 1996. An integrated approach for water quality: The PAM connection-West Stanislaus HUA, CA. Proc: Managing irrigation induced erosion and infiltration with polyacrylamide; Sojka RE & Lentz RD (eds). College of Southern Idaho, Twin Falls, 6-8 May. University of Idaho, Twin Falls, ID, USA. Misc Publ No 101-96, pp: 27-30.

Murtha J, 1995. Applications of fuzzy logic in operational meteorology. Sci Serv and Profes Development Newsletter 42-54.

Nash JE, Sutcliffe JV, 1970. River flow forecasting through conceptual models Part I - A discussion of principles. J Hydrol 10 (3): 282-290. https://doi.org/10.1016/0022-1694(70)90255-6

Ocampo-Duque W, Ferré-Huguet N, Domingo JL, Schuhmacher M, 2006. Assessing water quality in rivers with fuzzy inference systems: A case study. Environ Int 32: 733-742. https://doi.org/10.1016/j.envint.2006.03.009

Odhiambo LO, Yoder RE, Yoder DC, 2001. Estimation of reference crop evapotranspiration using fuzzy state models. T ASAE 44 (3): 543-550. https://doi.org/10.13031/2013.6114

Santos FL, Serralheiro RP, 2000. Improving infiltration of irrigated Mediterranean soils with polyacrylamide. J Agric Eng Res 76 (1): 83-90. https://doi.org/10.1006/jaer.2000.0534

Santos FL, Reis JL, Martins OC, Castanheira NL, Serralheiro RP, 2003. Comparative assessment of infiltration, runoff and erosion of sprinkler irrigated soils. Biosys Eng 86: 355-364. https://doi.org/10.1016/S1537-5110(03)00135-1

Sepaskhah AR, Bazrafshan-Jahromi AR, 2006. Controlling runoff and erosion in sloping land with polyacrylamide under a rainfall simulator. Biosys Eng 93: 469-474. https://doi.org/10.1016/j.biosystemseng.2006.01.003

Sepaskhah AR, Mahdi-Hosseinabadi Z, 2008. Effect of polyacrylamide on the erodibility factor of a loam soil. Biosys Eng 99 (4): 598-603. https://doi.org/10.1016/j.biosystemseng.2007.12.009

Sepaskhah AR, Shahabizad V, 2010. Effects of water quality and PAM application rate on the control of soil erosion, water infiltration and runoff for different soil textures measured in a rainfall simulator. Biosys Eng 106: 513-520. https://doi.org/10.1016/j.biosystemseng.2010.05.019

Shainberg I, Gal M, Ferreira AG, Goldstein D, 1991. Effect of water quality and amendments on the hydraulic properties and erosion from several Mediterranean soils. Soil Technol 4: 135-146. https://doi.org/10.1016/0933-3630(91)90025-I

Sojka RE, Bjorneberg DL, Entry JA, Lentz RD, Orts WJ, 2007. Polyacrylamide in agriculture and environmental land management. Adv Agron 92: 75-162. https://doi.org/10.1016/S0065-2113(04)92002-0

Tsoukalas LH, Uhrig RE, 1997. Fuzzy and neural approaches in engineering. John Wiley, NY.

Wallace A, Wallace GA, 1996. Need for solution or exchangeable calcium and/or critical EC level for flocculation of clay by polyacrylamides. Proc: Managing irrigation induced erosion and infiltration with polyacrylamide; Sojka RE & Lentz RD (eds). College of Southern Idaho, Twin Falls, 6-8 May. University of Idaho, Twin Falls, ID, USA. Misc Publ No 101-96, pp: 59-62.

Yen J, Langari R, 1999. Fuzzy logic: Intelligence, control and information. Prentice Hall.

Zadeh LA, 1965. Fuzzy sets. Inf Control 8: 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zadeh LA, 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst, Man, and Cybern SMC-3: 28-44. https://doi.org/10.1109/TSMC.1973.5408575




DOI: 10.5424/sjar/2017151-9179