Evaluation of Minor Losses in Connectors Used in Microirrigation Subunits Using Machine Learning Techniques
Publication: Journal of Irrigation and Drainage Engineering
Volume 147, Issue 8
Abstract
The proper hydraulic design of microirrigation system subunits requires the characterization of minor losses. To this end, machine learning models based on artificial neural networks [multilayer perceptron (MLP)], support vector machines [support vector regression (SVR)], and an ensemble of decision trees [extreme gradient boosting (XGB)] were developed and validated to predict minor losses caused by fittings commonly used in microirrigation subunits. The databases for learning are collections of experiments with commercial fittings classified as I, Y, and T. The features considered were fluid properties along with geometric and operational characteristics. Semiempirical models based on dimensional analysis were less accurate than machine learning–based models. The MLP model presented the best performance for the evaluated processes, although it requires a considerable amount of data and an extensive calibration of the hyperparameters. The SVR model was predominantly more appropriate based on the radial basis function. However, it is computationally expensive, and the estimator may be more compromised by noise. The XGB model achieved the lowest computational cost and provided good accuracy with the test set but was less related to the theoretical power-law function expected in these hydraulic phenomena. An open-source web application was developed to support the use and comparison of the models; it can serve as an online tool for the design and simulation of minor losses.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The databases can be partially visualized (free version) in https://plot.ly/∼wavila. Also, the source code from web application is available and can be directly seen inspecting the page http://mlosses.pythonanywhere.com/. The models based on dimensional analysis and machine learning techniques were generated from the tests provided by the Irrigation Testing Laboratory (LEMI/ESALQ/USP). Direct requests for these materials may be made to the provider as indicated in the acknowledgments. The corresponding author also agrees to dispose of the material if requested.
Acknowledgments
The authors are grateful to RSB Plásticos Ltd. for donating the fittings evaluated in this research. This work was supported by the Brazilian National Research Council (CNPq Grant No. 140329/2018-8).
References
Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, and J. Dean. 2016. “Tensorflow: A system for large-scale machine learning.” In Proc., 12th Symp. on Operating Systems Design and Implementation (OSDI 16), 265–283. Savannah, GA: USENIX.
Al-Amoud, A. I. 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.
Ardia, D., K. Boudt, P. Carl, K. Mullen, and B. Peterson. 2010. “Differential evolution with ‘Deoptim’: An application to non-convex portfolio optimization.” R J. 3 (1): 27–34. https://doi.org/10.32614/RJ-2011-005.
Bagarello, V., V. Ferro, G. Provenzano, and D. Pumo. 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).
Bengio, Y. 2012. “Practical recommendations for gradient-based training of deep architectures.” In Neural networks: Tricks of the trade, 437–478. Berlin: Springer.
Bombardelli, W., A. P. Camargo, R. Lavanholi, A. C. S. Araujo, M. Talamini Junior, and J. A. Frizzone. 2017. “Projeto e validação de uma bancada para ensaios de perda de carga localizada” Brazilian J. Irrig. Drain. 1: 1–10. https://doi.org/10.15809/irriga.2017v1n1p1-10.
Bombardelli, W. W., A. P. de Camargo, J. A. Frizzone, R. Lavanholi, and H. S. Rocha. 2019. “Local head loss caused in connections used in micro-irrigation systems.” Rev. Bras. Eng. Agríc. Ambient. 23 (7): 492–498. https://doi.org/10.1590/1807-1929/agriambi.v23n7p492-498.
Bombardelli, W. W., A. P. De Camargo, L. H. A. Rodrigues, and J. A. Frizzone. 2021a. “Evaluation of minor losses in connectors used in microirrigation subunits using machine learning techniques—Datasource.” Accessed May 6, 2021. https://plot.ly/~wavila.
Bombardelli, W. W., A. P. De Camargo, L. H. A. Rodrigues, and J. A. Frizzone. 2021b. “Evaluation of minor losses in connectors used in microirrigation subunits using machine learning techniques—Web application.” Accessed May 6, 2021. https://mlosses.pythonanywhere.com/.
Bullen, P., D. Cheeseman, L. Hussain, and A. Ruffellt. 1987. “The determination of pipe contraction pressure loss coefficients for incompressible turbulent flow.” Int. J. Heat Fluid Flow 8 (2): 111–118. https://doi.org/10.1016/0142-727X(87)90008-7.
Chen, T., and C. Guestrin. 2016. “Xgboost: A scalable tree boosting system.” In Proc., 22nd Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: Association for Computing Machinery.
Chen, Y., and Y. Hao. 2017. “A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction.” Expert Syst. Appl. 80 (Sep): 340–355. https://doi.org/10.1016/j.eswa.2017.02.044.
Chollet, F. 2018. Deep learning with Python. New York: Manning.
Cybenko, G. 1992. “Approximation by super positions of a sigmoidal function.” Math. Control Signals Syst. 5 (4): 455. https://doi.org/10.1007/BF02551274.
Demir, V., H. Yurdem, and A. Degirmencioglu. 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.
Duran-Ros, M., G. Arbat, J. Barragán, F. Ramírez de Cartagena, and J. Puig-Bargués. 2010. “Assessment of head loss equations developed with dimensional analysis for micro irrigation filters using effluents.” Biosyst. Eng. 106 (4): 521–526. https://doi.org/10.1016/j.biosystemseng.2010.06.001.
Dursun, M. 2014. “And efficient improved photovoltaic irrigation system with artificial neural network-based modeling of soil moisture distribution—A case study in Turkey.” Özden. Comput. Electron. Agric. 102 (Mar): 120–126. https://doi.org/10.1016/j.compag.2014.01.008.
Elnesr, M., and A. Alazba. 2017. “Simulation of water distribution under surface dripper using artificial neural networks.” Comput. Electron. Agric. 143 (Dec): 90–99. https://doi.org/10.1016/j.compag.2017.10.003.
Fan, J., X. Wang, L. Wu, H. Zhou, F. Zhang, X. Yu, X. Lu, and Y. Xiang. 2018a. “Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China.” Energy Convers. Manage. 164 (May): 102–111. https://doi.org/10.1016/j.enconman.2018.02.087.
Fan, J., W. Yue, L. Wu, F. Zhang, H. Cai, X. Wang, X. Lu, and Y. Xiang. 2018b. “Evaluation of SVM, elm and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China.” Agric. For. Meteorol. 263 (Dec): 225–241. https://doi.org/10.1016/j.agrformet.2018.08.019.
Ferro, V. 1997. “Applying hypothesis of self-similarity for flow-resistance law of small-diameter plastic pipes.” J. Irrig. Drain. Eng. 123 (3): 175–179. https://doi.org/10.1061/(ASCE)0733-9437(1997)123:3(175).
Fortin, F. A., F. M. de Rainville, M.-A. Gardner, M. Parizeau, and C. Gagné. 2012. “DEAP: Evolutionary algorithms made easy.” J. Mach. Learn. Res. 13 (1): 2171–2175.
Frizzone, J., P. D. Freitas, R. Rezende, and M. D. Faria. 2012. Microirrigação: gotejamento e microaspersão. Maringá, Brazil. Eduem.
García Nieto, P. J., E. García-Gonzalo, G. Arbat, M. Duran-Ros, F. R. de Cartagena, and J. Puig-Bargués. 2016. “A new predictive model for the filtered volume and outlet parameters in micro-irrigation sand filters fed with effluents using the hybrid PSO–SVM-based approach.” Comput. Electron. Agric. 125 (Jul): 74–80. https://doi.org/10.1016/j.compag.2016.04.031.
García Nieto, P. J., E. García-Gonzalo, G. Arbat, M. Duran-Ros, F. R. De Cartagena, and J. Puig-Bargués. 2018. “Pressure drop modelling in sand filters in micro-irrigation using gradient boosted regression trees.” Biosyst. Eng. 171 (Jul): 41–51. https://doi.org/10.1016/j.biosystemseng.2018.04.011.
Granata, F. 2019. “Evapotranspiration evaluation models based on machine learning algorithms—A comparative study.” Agric. Water Manage. 217 (May): 303–315. https://doi.org/10.1016/j.agwat.2019.03.015.
Guan, X., S. Wang, Z. Gao, and Y. Lv. 2013. “Dynamic prediction of soil salinization in an irrigation district based on the support vector machine.” Math. Comput. Modell. 58 (3–4): 719–724. https://doi.org/10.1016/j.mcm.2011.10.026.
Gunther, F., and S. Fritsch. 2010. “Neuralnet: Training of neural networks.” R J. 2 (1): 30–38.
Guo, H., L. Wang, J. Yu, F. Ye, C. Ma, and Z. Li. 2010. “Local resistance of fluid flow across sudden contraction in small channels.” Front. Energy Power Eng. China 4 (2): 149–154. https://doi.org/10.1007/s11708-009-0060-7.
Guzmán, S. M., J. O. Paz, M. L. M. Tagert, A. E. Mercer, and J. W. Pote. 2018. “An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels.” Agric. Syst. 159 (Jan): 248–259. https://doi.org/10.1016/j.agsy.2017.01.017.
Hinnell, A., N. Lazarovitch, A. Furman, M. Poulton, and A. Warrick. 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.
Hornik, K., M. Stinchcombe, and H. White. 1989. “Multilayer feedforward networks are universal approximators.” Neural Netw. 2 (5): 359–366. https://doi.org/10.1016/0893-6080(89)90020-8.
Juana, L., L. Rodriguez Sinobas, and A. Losada. 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(2002)128:6(376).
Juana, L., L. Rodriguez Sinobas, and A. Losada. 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).
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” In Proc., 3rd Int. Conf. for Learning Representations. Ithaca, NY: Cornell Univ.
Kuhn, M. 2008. “Building predictive models in R using the Caret package.” J. Stat. Software Articles 28 (5): 1–26. https://doi.org/10.18637/jss.v028.i05.
Kumar, M., N. Raghuwanshi, and R. Singh. 2011. “Artificial neural networks approach in evapotranspiration modeling: A review.” Irrig. Sci. 29 (1): 11–25. https://doi.org/10.1007/s00271-010-0230-8.
Larochelle, H., Y. Bengio, J. Louradour, and P. Lamblin. 2009. “Exploring strategies for training deep neural networks.” J. Mach. Learn. Res. 10 (1): 1–40.
Lavanholi, R., A. Pires de Camargo, W. W. Á. Bombardelli, J. A. Frizzone, N. Ait-Mouheb, E. Alberto da Silva, and F. Correia de Oliveira. 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.
Likhachev, E. 2003. “Dependence of water viscosity on temperature and pressure” Tech. Phys. 48 (4): 514–515. https://doi.org/10.1134/1.1568496.
Martí, P., G. Provenzano, and G. Palau-Salvador. 2009. “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.
Mattar, M. A., and A. I. Alamoud. 2015. “Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters.” Comput. Electron. Agric. 114 (Jun): 189–201. https://doi.org/10.1016/j.compag.2015.04.007.
Nguyen, P. M., A. Haghverdi, J. de Pue, Y.-D. Botula, K. V. Le, W. Waegeman, and W. M. Cornelis. 2017. “Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils.” Biosyst. Eng. 153 (Jan): 12–27. https://doi.org/10.1016/j.biosystemseng.2016.10.013.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, and B. Thirion. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Nov): 2825–2830.
Perboni, A., J. A. Frizzone, and A. P. de Camargo. 2014. “Artificial neural network-based equation to estimate head loss along drip irrigation laterals.” Revista Brasileira de Agricultura Irrigada 8 (2): 77. https://doi.org/10.7127/rbai.v8n200224.
Perboni, A., J. A. Frizzone, A. P. De Camargo, and M. F. Pinto. 2015. “Modelling head loss along emitting pipes using dimensional analysis.” Engenharia Agrícola 35 (5): 442–457. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n3p442-457/2015.
Pinto, M. F., A. P. D. Camargo, O. Rettore Neto, and J. A. Frizzone. 2014. “Caracterização hidráulica de tubos porosos oriundos de pneus reciclados utilizados em irrigação subsuperficial.” Revista Brasileira de Engenharia Agrícola e Ambiental. 18 (11): 1095–1101. https://doi.org/10.1590/1807-1929/agriambi.v18n11p1095-1101.
Plotly Technologies Inc. 2015. Collaborative data science. Montréal: Plotly Technologies.
Provenzano, G., V. Alagna, D. Autovino, J. M. Juarez, and G. Rallo. 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.
Provenzano, G., and D. Pumo. 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).
Puig-Bargués, J., J. Barragán, and F. R. de Cartagena. 2005. “Development of equations for calculating the head loss in effluent filtration in microirrigation systems using dimensional analysis.” Biosyst. Eng. 92 (3): 383–390. https://doi.org/10.1016/j.biosystemseng.2005.07.009.
Puig-Bargués, J., M. Duran-Ros, G. Arbat, J. Barragán, and F. R. de Cartagena. 2012. “Prediction by neural networks of filtered volume and outlet parameters in micro-irrigation sand filters using effluents.” Biosyst. Eng. 111 (1): 126–132. https://doi.org/10.1016/j.biosystemseng.2011.11.005.
Rettore Neto, O., J. H. de Miranda, J. A. Frizzone, and S. R. Workman. 2009a. “Local head loss of non-coaxial emitters inserted in polyethylene pipe.” Trans. ASABE 52 (3): 729. https://doi.org/10.13031/2013.27394.
Rettore Neto, O., J. A. Frizzone, J. H. Miranda, and T. A. Botrel. 2009b. “Perda de carga localizada em emissores não coaxiais integrados a tubos de polietileno.” Engenharia Agrícola. 29 (1): 28–39. https://doi.org/10.1590/S0100-69162009000100004.
Rocha, H. S. D., P. A. Marques, A. P. D. Camargo, J. A. Frizzone, and E. Saretta. 2017. “Internal surface roughness of plastic pipes for irrigation.” Revista Brasileira de Engenharia Agrícola e Ambiental. 21 (3): 143–149. https://doi.org/10.1590/1807-1929/agriambi.v21n3p143-149.
Sandulescu, V., and M. Chiru. 2016. Predicting the future relevance of research institutions—The winning solution of the KDD Cup 2016. Ithaca, NY: Cornell Univ.
Sobenko, L. R., W. W. Á. Bombardelli, A. Pires de Camargo, J. A. Frizzone, and S. N. Duarte. 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.
Sobenko, L. R., J. A. Frizzone, A. P. D. Camargo, E. Saretta, and H. S. Rocha. 2019. “Caracterização de injetor venturi utilizando análise dimensional.” Revista Brasileira de Engenharia Agrícola e Ambiental. 23 (7): 484–491. https://doi.org/10.1590/1807-1929/agriambi.v23n7p484-491.
Storn, R., and K. Price. 1997. “Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces.” Price J. Global Optim. 11 (3): 341–359. https://doi.org/10.1023/A:1008202821328.
Swamee, P. K. 1993. “Design of a submarine oil pipeline.” J. Transp. Eng. 119 (1): 159–170. https://doi.org/10.1061/(ASCE)0733-947X(1993)119:1(159).
Tanaka, M., G. Girard, R. Davis, A. Peuto, and N. Bignell. 2001. “Recommended table for the density of water between 0 c and 40 c based on recent experimental reports.” Metrologia 38 (4): 301. https://doi.org/10.1088/0026-1394/38/4/3.
Vekariya, P., R. Subbaiah, and H. Mashru. 2011. “Hydraulics of microtube emitters: A dimensional analysis approach.” Irrig. Sci. 29 (6): 341–350. https://doi.org/10.1007/s00271-010-0240-6.
Venkatanagendra, K., and D. Maligelaussenaiah. 2017. “Classification of rainfall data using linear kernel-based support vector machines.” Int. J. Appl. Eng. Res. 12 (Mar): 9717–9722.
Vilaça, F. N., A. P. De Camargo, J. A. Frizzone, L. Mateos, and R. Koech. 2017. “Minor losses in start connectors of microirrigation laterals.” Irrig. Sci. 35 (4): 227–240. https://doi.org/10.1007/s00271-017-0534-z.
Volkovs, M., G. W. Yu, and T. Poutanen. 2017. “Content-based neighbor models for cold start in recommender systems.” In Proc., Recommender Systems Challenge 2017. New York: Association for Computing Machinery. https://doi.org/10.1145/3124791.3124792.
Wu, W., W. Chen, H. Liu, S. Yin, and Y. Niu. 2014. “A new model for head loss assessment of screen filters developed with dimensional analysis in drip irrigation systems.” Irrig. Drain. 63 (4): 523–531. https://doi.org/10.1002/ird.1846.
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.
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.
Yurdem, H., V. Demir, and A. Degirmencioglu. 2008. “Development of a mathematical model to predict head losses from disc filters in drip irrigation systems using dimensional analysis.” Biosyst. Eng. 100 (3): 14–23. https://doi.org/10.1016/j.biosystemseng.2008.01.003.
Zanetti, S., E. Sousa, V. Oliveira, F. Almeida, and S. Bernardo. 2007. “Estimating evapotranspiration using artificial neural network and minimum climatological data.” J. Irrig. Drain. Eng. 133 (2): 83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83).
Zitterell, D. B., J. A. Frizzone, and O. Rettore Neto. 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.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 147 • Issue 8 • August 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 24, 2020
Accepted: Mar 9, 2021
Published online: Jun 8, 2021
Published in print: Aug 1, 2021
Discussion open until: Nov 8, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.