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

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

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dc.contributor.author Martí Pérez, Pau Carles es_ES
dc.contributor.author Shiri, Jalal es_ES
dc.contributor.author Roman Alorda, Armando es_ES
dc.contributor.author Turegano Pastor, José Vicente es_ES
dc.contributor.author Royuela, Alvaro es_ES
dc.date.accessioned 2023-12-27T19:01:35Z
dc.date.available 2023-12-27T19:01:35Z
dc.date.issued 2023-11 es_ES
dc.identifier.issn 0342-7188 es_ES
dc.identifier.uri http://hdl.handle.net/10251/201173
dc.description.abstract [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. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Irrigation Science es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Artificial neural-networks es_ES
dc.subject Drip irrigation laterals es_ES
dc.subject Reference evapotranspiration es_ES
dc.subject Noncoaxial emitters es_ES
dc.subject System uniformity es_ES
dc.subject Wetting patterns es_ES
dc.subject Pressure losses es_ES
dc.subject In-line es_ES
dc.subject Simulation es_ES
dc.subject Water es_ES
dc.subject.classification INGENIERIA AGROFORESTAL es_ES
dc.title Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00271-023-00852-z es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.description.bibliographicCitation Martí 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-z es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00271-023-00852-z es_ES
dc.description.upvformatpinicio 783 es_ES
dc.description.upvformatpfin 801 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 41 es_ES
dc.description.issue 6 es_ES
dc.relation.pasarela S\505777 es_ES
dc.contributor.funder Universitat Politècnica de València
dc.description.references Al-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.1995.1090 es_ES
dc.description.references Al-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.2017.10.005 es_ES
dc.description.references ASAE EP 405.1 1988 (R2019). Design and Installation of Microirrigation Systems. American Society of Agricultural Engineers. USA es_ES
dc.description.references Ayars 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 1–26 es_ES
dc.description.references Bagarello 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) es_ES
dc.description.references Baiamonte G (2018) Advances in designing drip irrigation laterals. Agric Water Manag 199:157–174. https://doi.org/10.1016/j.agwat.2017.12.015 es_ES
dc.description.references Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, UK es_ES
dc.description.references Bombardelli 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.v23n7p492-498 es_ES
dc.description.references Bombardelli 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.1943-4774.0001591 es_ES
dc.description.references Demir 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.2007.01.002 es_ES
dc.description.references Elnesr 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.2017.10.003 es_ES
dc.description.references Ferreira 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/0102027 es_ES
dc.description.references Ferreira C (2001b) Gene expression programming in problem solving. 6th online world conference on soft computing in industrial applications. Springer, Berlin es_ES
dc.description.references Gomes 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/S0100-69162010000300008 es_ES
dc.description.references Guan 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.2013.03.020 es_ES
dc.description.references Guan 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-X es_ES
dc.description.references Gyasi-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) es_ES
dc.description.references Hagan MT, Delmuth H, Beale M (1996) Neural network design. PWS Publishing Company, Boston, MA es_ES
dc.description.references Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall International Inc., New Jersey es_ES
dc.description.references Hinnell 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/s00271-010-0214-8 es_ES
dc.description.references Hornik 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)90020-8 es_ES
dc.description.references Juana 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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. es_ES
dc.description.references Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Bradford Book, Cambridge, MA es_ES
dc.description.references Lavanholi 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.1943-4774.0001485 es_ES
dc.description.references Martí 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.1943-4774.0000125 es_ES
dc.description.references Martí 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.2012.12.001 es_ES
dc.description.references Martí 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.2013.08.016 es_ES
dc.description.references Martí 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.2014.10.028 es_ES
dc.description.references Mattar 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.2017.12.017 es_ES
dc.description.references Mattar 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.2015.04.007 es_ES
dc.description.references Mattar 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.2014.09.015 es_ES
dc.description.references Mattar 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/s00271-019-00647-1 es_ES
dc.description.references Nunes 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.1943-4774.0001516 es_ES
dc.description.references Palau-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) es_ES
dc.description.references 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.v8n200224 es_ES
dc.description.references Perboni 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.v35n3p442-457/2015 es_ES
dc.description.references Provenzano 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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.1943-4774.0000731 es_ES
dc.description.references Provenzano 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.1943-4774.0000958 es_ES
dc.description.references Rettore 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/10.13031/2013.27394 es_ES
dc.description.references Rodriguez-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):289–296 es_ES
dc.description.references Royuela 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. Spain es_ES
dc.description.references Samadianfard 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/2012104-502-11 es_ES
dc.description.references Sayyadi 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/2012104-445-11 es_ES
dc.description.references Shao J (1993) Linear model selection by cross-validation. J Am Stat Assoc 88(422):486–494. https://doi.org/10.1016/j.jspi.2003.10.004 es_ES
dc.description.references Shiri 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.2011.11.004 es_ES
dc.description.references Shiri 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.9669 es_ES
dc.description.references Sobenko 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.1943-4774.0001466 es_ES
dc.description.references Stone 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.x es_ES
dc.description.references Vilaça 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-z es_ES
dc.description.references Wang 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/s00271-019-00656-0 es_ES
dc.description.references Wang 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.2014.04.013 es_ES
dc.description.references Wang 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.2273 es_ES
dc.description.references Wang 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.2019.11.021 es_ES
dc.description.references Yassin 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.2016.01.035 es_ES
dc.description.references Yassin 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.2015.09.009 es_ES
dc.description.references Yildirim 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.303 es_ES
dc.description.references Yildirim 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/s00271-009-0197-5 es_ES
dc.description.references Zitterell 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-y es_ES


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