2 research outputs found

    Modeling sprinkler irrigation infiltration based on a fuzzy-logic approach

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    Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches

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    [EN] The presence of emitters along the lateral, as well as of connectors along the manifold, causes additional local head losses other than friction losses. An accurate estimation of local losses is of crucial importance for a correct design of microirrigation systems. This paper presents a procedure to assess local head losses caused by 6 lateral start connectors of 32- and 40-mm nominal diameter each under actual hydraulic working conditions based on artificial neural networks (ANN) and gene expression programming (GEP) modelling approaches. Different input-output combinations and data partitions were assessed to analyse the hydraulic performance of the system and the optimum training strategy of the models, respectively. The range of the head losses in the manifold (hs(M)) is considerable lower than in the lateral (hs(L)). hs(M) increases with the protrusion ratio (s/S). hs(L) does not decrease for a decreasing s/S. There is a correlation between hs(L) and the Reynolds number in the lateral (Re-L). However, this correlation might also be dependent on the flow conditions in the manifold before the derivation. The value of the head loss component due to the protrusion might be influenced by the flow derivation. DN32 connectors and hs(M) present more accurate estimates. Crucial input parameters are flow velocity and protrusion ratio. The inclusion of friction head loss as input also improves the estimating accuracy of the models. The range of the indicators is considerably worse for DN40 than for DN32. The models trained with all patterns lead to more accurate estimations in connectors 7 to 12 than the models trained exclusively with DN40 patterns. On the other hand, including DN40 patterns in the training process did not involve any improvement for estimating the head losses of DN32 connectors. ANN were more accurate than GEP in DN32. In DN40 ANN were less accurate than GEP for hs(M), but they were more accurate than GEP for hs(L), while both presented a similar performance for hs(combined). Different equations were obtained using GEP to easily estimate the two components of the local loss. The equation that should be used in practice depends on the availability of inputs.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureMartí Pérez, PC.; Shiri, J.; Roman Alorda, A.; Turegano Pastor, JV.; Royuela, A. (2023). Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches. Irrigation Science. 41(6):783-801. https://doi.org/10.1007/s00271-023-00852-z783801416Al-Amoud AI (1995) Significance of energy losses due to emitter connections in trickle irrigation lines. J Agric Eng Res 60(1):1–5. https://doi.org/10.1006/jaer.1995.1090Al-Ghobari HM, El-Marazky MS, Dewidar AZ, Mattar MA (2018) Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques. Agric Water Manag 195:211–221. https://doi.org/10.1016/j.agwat.2017.10.005ASAE EP 405.1 1988 (R2019). Design and Installation of Microirrigation Systems. American Society of Agricultural Engineers. USAAyars JE, Bucks DA, Lamm FR, Nakayama FS (2007) Introduction. In: Lamm FR, Ayars JE, Nakayama FS (eds) Microirrigation for crop production: design, operation, and management. Elsevier, Amsterdam, pp 1–26Bagarello V, Ferro V, Provenzano G, Pumo D (1997) Evaluating pressure losses in drip-irrigation lines. J Irrig Drain Eng 123(1):1–7. https://doi.org/10.1061/(ASCE)0733-9437(1997)123:1(1)Baiamonte G (2018) Advances in designing drip irrigation laterals. Agric Water Manag 199:157–174. https://doi.org/10.1016/j.agwat.2017.12.015Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, UKBombardelli WWA, de Camargo AP, Frizzone JA, Lavanholi R, Rocha HS (2019) Local head loss caused in connections used in micro-irrigation systems. Rev Bras Eng Agric Ambient 23(7):492–498. https://doi.org/10.1590/1807-1929/agriambi.v23n7p492-498Bombardelli WWA, de Camargo AP, Rodrigues LHA, Frizzone JA (2021) Evaluation of minor losses in connectors used in microirrigation subunits using machine learning techniques. J Irrig Drain Eng 147(8):04021032. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001591Demir V, Yurdem H, Degirmencioglu A (2007) Development of prediction models for friction losses in drip irrigation laterals equipped with integrated in-line and on-line emitters using dimensional analysis. Biosyst Eng 96(1):617–631. https://doi.org/10.1016/j.biosystemseng.2007.01.002Elnesr M, Alazba A (2017) Simulation of water distribution under surface dripper using artificial neural networks. Comput Electron Agric 143(12):90–99. https://doi.org/10.1016/j.compag.2017.10.003Ferreira C (2001a) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129. https://doi.org/10.48550/arXiv.cs/0102027Ferreira C (2001b) Gene expression programming in problem solving. 6th online world conference on soft computing in industrial applications. Springer, BerlinGomes AWA, Frizzone JA, Rettore Neto O, Miranda JH (2010) Local head losses for integrated drippers in polyethylene pipes. Eng Agrícola 30(3):435–446. https://doi.org/10.1590/S0100-69162010000300008Guan H, Li J, Li Y (2013a) Effects of drip system uniformity and irrigation amount on cotton yield and quality under arid conditions. Agric Water Manag 124:37–51. https://doi.org/10.1016/j.agwat.2013.03.020Guan H, Li J, Li Y (2013b) Effects of drip system uniformity and irrigation amount on water and salt distributions in soil under arid conditions. J Integr Agric 12(5):924–939. https://doi.org/10.1016/S2095-3119(13)60310-XGyasi-Agyei Y (2007) Field-scale assessment of uncertainties in drip irrigation lateral parameters. J Irrig Drain Eng 133(6):512–520. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:6(512)Hagan MT, Delmuth H, Beale M (1996) Neural network design. PWS Publishing Company, Boston, MAHaykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall International Inc., New JerseyHinnell A, Lazarovitch N, Furman A, Poulton M, Warrick A (2010) Neuro-drip: estimation of subsurface wetting patterns for drip irrigation using neural networks. Irrig Sci 28(6):535–544. https://doi.org/10.1007/s00271-010-0214-8Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366. https://doi.org/10.1016/0893-6080(89)90020-8Juana L, Rodriguez-Sinobas L, Losada A (2002a) Determining minor head losses in drip irrigation laterals. I: methodology. J Irrig Drain Eng 128(6):376–384. https://doi.org/10.1061/(ASCE)0733-9437(2002a)128:6(376)Juana L, Rodriguez-Sinobas L, Losada A (2002b) Determining minor head losses in drip irrigation laterals. II: experimental study and validation. J Irrig Drain Eng 128(6):385–396. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:6(385)Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the fourteenth international joint conference on artificial intelligence. Morgan Kaufmann, San Mateo, CA. 2(12) p.1137–1143.Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Bradford Book, Cambridge, MALavanholi R, Pires de Camargo A, Bombardelli WWA, Frizzone JA, Ait-Mouheb N, Alberto da Silva E, Correia de Oliveira F (2020) Prediction of pressure–discharge curves of trapezoidal labyrinth channels from nonlinear regression and artificial neural networks. J Irrig Drain Eng 146(8):04020018. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001485Martí P, Provenzano G, Royuela A, Palau-Salvador G (2010) Integrated emitter local loss prediction using artificial neural networks. J Irrig Drain Eng 136(1):11–22. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000125Martí P, Gasque M, González-Altozano P (2013a) An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. Comput Electron Agric 91(2):75–86. https://doi.org/10.1016/j.compag.2012.12.001Martí P, Shiri J, Duran-Ros M, Arbat G, Ramírez de Cartagena F, Puig-Bargués J (2013b) Artificial neural networks vs. gene expression programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput Electron Agric 99(11):176–185. https://doi.org/10.1016/j.compag.2013.08.016Martí P, González-Altozano P, López-Urrea R, Mancha L, Shiri J (2015) Modeling reference evapotranspiration with calculated targets. Assessment and implications. Agric Water Manag 149(2):81–90. https://doi.org/10.1016/j.agwat.2014.10.028Mattar MA (2018) Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agric Water Manag 198:28–38. https://doi.org/10.1016/j.agwat.2017.12.017Mattar MA, Alamoud AI (2015) Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters. Comput Electron Agric 114(6):189–201. https://doi.org/10.1016/j.compag.2015.04.007Mattar MA, Alazba AA, Zin El-Abedin TK (2015) Forecasting furrow irrigation infiltration using artificial neural networks. Agric Water Manag 148(1):63–71. https://doi.org/10.1016/j.agwat.2014.09.015Mattar MA, Alamoud AI, Al-Othman AA, Elansary HO, Farah AHH (2020) Hydraulic performance of labyrinth-channel emitters: experimental study, ANN, and GEP modeling. Irrig Sci 38:1–16. https://doi.org/10.1007/s00271-019-00647-1Nunes Flores JH, Coll Faria L, Rettore Neto O, Diotto AV, Colombo A (2021) Methodology for determining the emitter local head loss in drip irrigation systems. J Irrig Drain Eng 147(1):060220014. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001516Palau-Salvador G, Sanchis LH, Gonzalez-Altozano P, Arviza J (2006) Real local losses estimation for on-line emitters using empirical a numerical procedures. J Irrig Drain Eng 132(6):522–530. https://doi.org/10.1061/(ASCE)0733-437(2006)132:6(522)Perboni A, Frizzone JA, de Camargo AP (2014) Artificial neural network-based equation to estimate head loss along drip irrigation laterals. Revista Brasileira De Agricultura Irrigada 8(2):77–85. https://doi.org/10.7127/rbai.v8n200224Perboni A, Frizzone JA, De Camargo AP, Pinto MF (2015) Modelling head loss along emitting pipes using dimensional analysis. Eng Agrícola 35(5):442–457. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n3p442-457/2015Provenzano G, Pumo D (2004) Experimental analysis of local pressure losses for microirrigation laterals. J Irrig Drain Eng 130(4):318–324. https://doi.org/10.1061/(ASCE)0733-9437(2004)130:4(318)Provenzano G, Pumo D, Di Pio P (2005) Simplified procedure to evaluate head losses in drip irrigation laterals. J Irrig Drain Eng 131(6):525–532. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:6(525)Provenzano G, Di Dio P, Palau-Salvador G (2007) New computational fluid dynamic procedure to estimate friction and local losses in coextruded drip laterals. J Irrig Drain Eng 133(6):520–527. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:6(520)Provenzano G, Di Dio P, Leone R (2014) Assessing a local losses evaluation procedure for low-pressure lay-flat drip laterals. J Irrig Drain Eng 140(6):04014017. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000731Provenzano G, Alagna V, Autovino D, Manzano Juárez J, Rallo G (2016) Analysis of geometrical relationships and friction losses in small-diameter lay-flat polyethylene pipes. J Irrig Drain Eng 142(2):04015041. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000958Rettore Neto O, de Miranda JH, Frizzone JA, Workman SR (2009) Local head loss of non-coaxial emitters inserted in polyethylene pipe. Trans 52(3):729. https://doi.org/10.13031/2013.27394Rodriguez-Sinobas L, Juana L, Sánchez-Calvo R, Losada A (2004) Pérdidas de carga localizadas en inserciones de ramales de goteo. Ingeniería Del Agua 11(3):289–296Royuela A, Martí P, Manzano J (2010) Pérdidas de carga singulares en la entrada de los laterales de riego localizado conectados mediante collarín de toma. XVIII Congreso Nacional de Riegos, pp 147–148. SpainSamadianfard S, Sadraddini AA, Nazemi AH, Provenzano G, Kişi Ö (2014) Estimating soil wetting patterns for drip irrigation using genetic programming. Span J Agric Res 10(4):1155–1166. https://doi.org/10.5424/sjar/2012104-502-11Sayyadi H, Sadraddini AA, Zadeh DF, Montero J (2012) Artificial neural networks for simulating wind effects on sprinkler distribution patterns. Span J Agric Res 10(4):1143–1154. https://doi.org/10.5424/sjar/2012104-445-11Shao J (1993) Linear model selection by cross-validation. J Am Stat Assoc 88(422):486–494. https://doi.org/10.1016/j.jspi.2003.10.004Shiri J, Kisi O, Landeras G, Lopez JJ, Nazemi AH, Stuyt LCPM (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J Hydrol 414–415:302–316. https://doi.org/10.1016/j.jhydrol.2011.11.004Shiri J, Martí P, Singh VP (2014) Evaluation of gene expression programming approaches for estimating daily pan evaporation through spatial and temporal data scanning. Hydrol Process 28(3):1215–1225. https://doi.org/10.1002/hyp.9669Sobenko LR, Bombardelli WWA, Pires de Camargo A, Frizzone JA, Duarte SN (2020) Minor losses through start connectors in microirrigation laterals: dimensional analysis and artificial neural networks approaches. J Irrig Drain Eng 146(5):04020005. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001466Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B 36:111–147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.xVilaça FN, De Camargo AP, Frizzone JA, Mateos L, Koech R (2017) Minor losses in start connectors of microirrigation laterals. Irrig Sci 35(4):227–240. https://doi.org/10.1007/s00271-017-0534-zWang J, Chen R (2020) An improved finite element model for the hydraulic analysis of drip irrigation subunits considering local emitter head loss. Irrig Sci 38:147–162. https://doi.org/10.1007/s00271-019-00656-0Wang Z, Li J, Li Y (2014) Simulation of nitrate leaching under varying drip system uniformities and precipitation patterns during the growing season of maize in the north China plain. Agric Water Manag 142:19–28. https://doi.org/10.1016/j.agwat.2014.04.013Wang Y, Zhu DL, Zhang L, Zhu S (2018) Simulation of local head loss in trickle lateral lines equipped with in-line emitters based on dimensional analysis. Irrig and Drain 67(4):572–581. https://doi.org/10.1002/ird.2273Wang J, Yang T, Wei T, Chen R, Yuan S (2020) Experimental determination of local head loss of non-coaxial emitters in thin-wall lay-flat polyethylene pipes. Biosyst Eng 190(2):71–86. https://doi.org/10.1016/j.biosystemseng.2019.11.021Yassin MA, Alazba AA, Mattar MA (2016a) A new predictive model for furrow irrigation infiltration using gene expression programming. Comput Electron Agric 122:168–175. https://doi.org/10.1016/j.compag.2016.01.035Yassin MA, Alazba AA, Mattar MA (2016b) Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agric Water Manag 163(1):110–124. https://doi.org/10.1016/j.agwat.2015.09.009Yildirim G (2007) An assessment of hydraulic design of trickle laterals considering effect of minor losses. Irrig Drain 56(4):399–421. https://doi.org/10.1002/ird.303Yildirim G (2010) Total energy loss assessment for trickle lateral lines equipped with integrated in-line and on-line emitters. Irrig Sci 28(5):341–352. https://doi.org/10.1007/s00271-009-0197-5Zitterell DB, Frizzone JA, Rettore Neto O (2014) Dimensional analysis approach to estimate local head losses in microirrigation connectors. Irrig Sci 32(4):169–179. https://doi.org/10.1007/s00271-013-0424-
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