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Comparing data driven models versus numerical models in simulation of waterfront advance in furrow irrigation

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Abstract

Accurate design, appropriate management, and knowledge of relationships between the parameters affecting on the performance of a surface irrigation system are the factors which play an effective role in increasing the efficiency of these systems. If parameters such as advance distance can be well estimated per specified flow rate, the volume of infiltrated water can be estimated, thereby preventing water loss and enhancing irrigation efficiency to a great extent. In the present study evaluated the accuracy of data-driven methods Random Forest (RF), Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and M5 Model Tree and common numerical methods such as the Full hydrodynamic and Zero-inertia model (using SIRMOD software) and Zero-inertial model (using WinSRFR software) to predict the advance distance in furrow irrigation. To this end, seven series of data resulting from the evaluation of furrow irrigation system in various regions were collected. Each series included 12 input parameters of furrow length (L), furrow geometrical cross-section coefficients (\(\sigma_{1} ,\sigma_{2}\)), furrow hydraulic cross-section coefficients (\(\rho_{1} ,\rho_{2}\)), inflow rate (Q), Maning’s coefficient (n), field slope (\(S_{0}\)), cut-off time \((T_{\text{cutoff}} )\), final infiltration rate (\(f_{0}\)), and the infiltration parameters of the Kostiakov equation (a and k). Comparison of the results showed that all the data-driven methods managed to estimate the advance distance of the wetting front in the furrow with higher accuracy than the numerical methods. From among these, the ANFIS model had the highest accuracy (RMSE = 1.842 m, MAE = 1.305 m) in estimating the advance distance in the furrow.

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References

  • Abbasi F, Shooshtari MM, Feyen J (2003) Evaluation of various surface irrigation numerical simulation models. J Irrig Drain Eng 129(3):208–213. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:3(208)

    Article  Google Scholar 

  • Bautista E, Clemmens AJ, Strelkoff TS, Schlegel J (2009) Modern analysis of surface irrigation systems with Win SRFR. Agr Water Manage 96:1146–1154. https://doi.org/10.1016/j.agwat.2009.03.007

    Article  Google Scholar 

  • Bautista E, Strelkoff TS, Schlegel JL (2012) Current developments in software for surface irrigation analysis: WinSRFR 4/SRFR 5 world environmental and water resources congress: crossing boundaries. ASCE, 2128–2137. 10.1061/9780784412312.213

  • Breiman L (2001) Random forests. J Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Caudill M (1987) Neural networks primer, part I. AI Expert 2(12):46–52. ‏https://dl.acm.org/citation.cfm?id=38295

  • Chow VT (1959) Open channel hydraulics. McGraw-Hill, New York

    Google Scholar 

  • Clemmens AJ, Dedrick AR, Strand RJ (1995) BASIN—a computer program for the design of level-basin irrigation systems. version 2.0. WCL Report 19. USDA–ARS

  • Dayhoff JE (1990) Neural network principles. Prentice-Hall Press, New York

    Google Scholar 

  • Elliott RL, Walker WR (1982) Field evaluation of furrow infiltration and advance function. Trans ASCE 25(2):396–400

    Article  Google Scholar 

  • Elliott RL, Walker WR, Skogerboe GV (1982) Zero-inertia modeling of furrow irrigation advance. J Irrig Drain Div 108(3):179–195

    Google Scholar 

  • Esfandiari M, Maheshwari BL (2001) Field evaluation of furrow irrigation models. J Agr Eng Res 79:459–479

    Article  Google Scholar 

  • Ebrahimian H, Liaghat A (2011) Field evaluation of various mathematical models for furrow and border irrigation systems. Soil Water Res 6(2):91–101. https://doi.org/10.17221/34/2010-SWR

    Article  Google Scholar 

  • Furman A (2008) Modeling coupled surface-subsurface flow processes: a review. Vadose Zone J 7(2):741–756. https://doi.org/10.2136/vzj2007.0065

    Article  Google Scholar 

  • Haznedar B, Kalinli A (2016) Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification. Int J Intell Syst Appl Eng 4:44–47. https://doi.org/10.18201/ijisae.266053

    Article  Google Scholar 

  • Hornbuckle, JW, Christen EW, Faulkner RD (2005) Use of SIRMOD as a quasi real time surface irrigation decision support system. In: Zerger A, Argent RM (eds) MODSIM 2005. International congress on modelling and simulation. Modelling and Simulation Society of Australia and New Zealand, pp 217–223

  • Jang JSR (1993) ANFIS: adaptive-network—based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–684

    Article  Google Scholar 

  • Jurriens M, Zerihun D, Boonstra J, Feyen J (2001) SURDEV: surface irrigation software, design, operation and evaluation of basin, border, and furrow irrigation. International institute for land reclamation and improvement, ILRI, Wageningen

    Google Scholar 

  • Khanna T (1990) Foundations of neural networks reading. Addison-Wesley, Massachusetts, MA

    Google Scholar 

  • King BA, Bjorneberg DL, Trout TJ, Mateos L, Araujo DF, Costa RN (2015) Estimation of furrow irrigation sediment loss using an artificial neural network. J Irrig Drain Eng 142(1):04015031–04015038. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000932

    Article  Google Scholar 

  • Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40:438–444. https://doi.org/10.1016/j.advengsoft.2008.06.004

    Article  Google Scholar 

  • Latif M, Mahmood S (2004) Field measurement and simulation of advance rate for continuous and surge irrigated furrows in Pakistan. Irrig And Drain 53:437–447. https://doi.org/10.1002/ird.140

    Article  Google Scholar 

  • Mahdizadeh Khasraghi M, GholamiSefidkouhi MA, Valipour M (2015) Simulation of open- and closed—end border irrigation systems using SIRMOD. Arch Agron Soil Sci 61(7):929–941. https://doi.org/10.1080/03650340.2014.981163

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mehana HM, EL-Bagoury KF, Hussein MM, EI-Gindy AM (2009) Validation of surface irrigation model sirmod under clay loam soil conditions in Egypt. J Irrig Drain Eng 26(3):1299–1317

    Google Scholar 

  • Moravejalahkami B, Mostafazadeh-fard B, Heidarpour M, Abbasi F (2012) The effects of different inflow hydrograph shapes on furrow irrigation fertigation. Biosyst Eng 111(2):186–194. https://doi.org/10.1016/j.biosystemseng.2011.11.011

    Article  Google Scholar 

  • Najafi G, Ghobadian B, Tavakoli T, Buttsworth DR, Yusaf TF, Faizollahnejad M (2009) Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Appl Energy 86(5):630–639. https://doi.org/10.1016/j.apenergy.2008.09.017

    Article  CAS  Google Scholar 

  • Oweis TY (1983) Surge flow furrow irrigation hydraulics with zero inertia. Doctoral Thesis presented to Utah State University, Logan, UT, pp 621

  • Oweis TY, Walker WR (1990) Zero- inertia model for surge flow furrow irrigation. Irrig Sci 11:131–136. https://doi.org/10.1007/BF00189449

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. In: Proceeding of Australian joint conference on artificial intelligence, 16–18 November, pp 343–348

  • Sablani SS, Ramaswamy HS, Sreekanth S, Prasher SO (1997) Neural network modeling of heat transfer to liquid particle mixture in cans subjected to end-over-end processing. Food Res Int 30(2):105–116. https://doi.org/10.1016/S0963-9969(97)00029-X

    Article  Google Scholar 

  • Sayari S, Rahimpour M, Zounemat-Kermani M (2017) Numerical modeling based on a finite element method for simulation of flow in furrow irrigation. Paddy Water Environ 15(4):879–887. https://doi.org/10.1007/s10333-017-0599-6

    Article  Google Scholar 

  • Shaalan K, Riad M, Amer A, Baraka H (1999) Speculative work in neural network forecasting: an application to Egyptian cotton production. Egypt Comput J 27(1):58–79

    Google Scholar 

  • Souza F (1981) Nonlinear hydrodynamic model of furrow irrigation, Ph.D. thesis, University of California, Davis, California, USA

  • Strelkoff T (1969) One-dimensional equation of open channel flow. J Hydraul Div 95:861–876

    Google Scholar 

  • Strelkoff TS (1992) EQSWP: extended unsteady—flow double—sweep equation slover. J Hydraul Eng 118(5):735–742

    Article  Google Scholar 

  • Strelkoff T, Katapodes ND (1977) Border irrigation hydraulics with zero inertia. J Irrig Drain Div 103(IR3):325–342

    Google Scholar 

  • Strelkoff TS, Clemmens AJ, Schmidt BV, Slosky EJ (1996) Border: a design and management aid for sloping border irrigation systems. WCL Report, 21

  • Suparta W, Alhasa KM (2016) Modeling of tropospheric delays using ANFIS. Springer International Publishing

  • Svetnik V, Liaw A, Tong C, Culberson J, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Com Sci 43(6):1947–1958. https://doi.org/10.1021/ci034160g

    Article  CAS  Google Scholar 

  • Taki M, Ajabshirchi Y, Ranjbar SF, Rohani A, Matlooi M (2016) Heat transfer and MLP neural network models to predict inside environment and energy lost in a semi-solar greenhouse. Energ Buildings 110(1):314–329. https://doi.org/10.1016/j.enbuild.2015.11.010

    Article  Google Scholar 

  • Taki M, Rohani A, Soheili-Fard F, Abdeshahi A (2018) Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models. J Clean Prod 172:3028–3041. https://doi.org/10.1016/j.jclepro.2017.11.107

    Article  Google Scholar 

  • Valipour M (2012) Comparison of surface irrigation simulation models: full Hydrodynamic, Zero Inertia, Kinematic Wave. J Agric Sci 4(12):68–74. https://doi.org/10.5539/jas.v4n12p68

    Article  Google Scholar 

  • Valipour M, Montazar AA (2012a) An evaluation of SWDC and WinSRFR models to optimize of infiltration parameters in Furrow irrigation. AM J Sci Res 69:128–142

    Google Scholar 

  • Valipour M, Montazar AA (2012b) Optimize of all effective infiltration parameters in furrow irrigation using Visual Basic and genetic algorithm programming. Austral J Basic Appl Sci 6(6):132–137

    Google Scholar 

  • Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forests: a survey and results of new tests. Pattern Recognit 44:330–349. https://doi.org/10.1016/j.patcog.2010.08.011

    Article  Google Scholar 

  • Walker WR (1993) SIRMOD, a surface irrigation model. Utah State University, Department of Biological and Irrigation Engineering, Logan

    Google Scholar 

  • Walker WR (2003) Surface irrigation simulation, evaluation and design. User Guide and Technical Documentation. Utah State University, Logan, Utah, pp 145

    Google Scholar 

  • Walker WR, Humphreys AS (1983) Kinematic wave furrow irrigation model. J Irrig Drain Div 109(4):377–392

    Article  Google Scholar 

  • Walker WR, Lee TS (1981) Kinematic-wave approximation of surged furrow advance. InASAE Winter Meeting Paper No. 81-2544

  • Walker WR, Skogerboe GV (1987) Surface irrigation: theory and practice. prentice-hall Inc, Englewood Cliffs

    Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Yassin MA, Alazba AA, Mattar MA (2016) 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

    Article  Google Scholar 

  • Zounemat-Kermani M (2012) Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol Atmos Phys 117(3–4):181–192. https://doi.org/10.1007/s00703-012-0192-x

    Article  Google Scholar 

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Acknowledgements

Authors are thankful Elliott et al. (1982), Walker and Skogerboe (1987), Abbasi et al. (2003) and Moravejalahkami et al. (2012) for using their datasets.

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Authors and Affiliations

  1. Water Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

    Soudabeh Golestani Kermani, Sareh Sayari & Mohammad Zounemat-Kermani

  2. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

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  1. Soudabeh Golestani Kermani
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  2. Sareh Sayari
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  3. Ozgur Kisi
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  4. Mohammad Zounemat-Kermani
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Correspondence to Soudabeh Golestani Kermani.

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Communicated by G. Merkley.

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Golestani Kermani, S., Sayari, S., Kisi, O. et al. Comparing data driven models versus numerical models in simulation of waterfront advance in furrow irrigation. Irrig Sci 37, 547–560 (2019). https://doi.org/10.1007/s00271-019-00635-5

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