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Forecasting furrow irrigation infiltration using artificial neural networks

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  • Mattar, M.A.
  • Alazba, A.A.
  • Zin El-Abedin, T.K.

Abstract

An artificial neural network (ANN) was developed for estimating the infiltrated water volume (Z) under furrow irrigation. A feed-forward neural network using back-propagation training algorithm was developed for the prediction. Four variables were used as input parameters; inflow rate (Qo), furrow length (L), waterfront advance time at the end of the furrow (TL) and infiltration opportunity time (To). The Z was the one node in the output layer. The data used to develop the ANN model were taken from published experiments. The ANN model predicted Z over a wide range of the input variables with statistical analysis indicating that it can successfully predict Z with a high degree of accuracy. Performance evaluation criteria indicated that the ANN model was better than the two-point method using a volume balance model. Using testing and validation data sets to compare the ANN model with the two-point method shows that the two-point method had a mean coefficient of determination (R2) value that was about 3.6% less accurate than that from the ANN model. Also, the mean root mean square error (RMSE) value of 0.0135m3m−1 for the two-point method was almost double that of mean values for the ANN model. The relative errors of computed Z values for the ANN model were mostly around ±10%. Therefore, the ANN model is applicable to other soils and to different furrow irrigation hydraulics.

Suggested Citation

  • Mattar, M.A. & Alazba, A.A. & Zin El-Abedin, T.K., 2015. "Forecasting furrow irrigation infiltration using artificial neural networks," Agricultural Water Management, Elsevier, vol. 148(C), pages 63-71.
    • Handle: RePEc:eee:agiwat:v:148:y:2015:i:c:p:63-71
    DOI: 10.1016/j.agwat.2014.09.015
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    References listed on IDEAS

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    1. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
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    2. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    3. Zhongwei Liang & Tao Zou & Yupeng Zhang & Jinrui Xiao & Xiaochu Liu, 2022. "Sprinkler Drip Infiltration Quality Prediction for Moisture Space Distribution Using RSAE-NPSO," Agriculture, MDPI, vol. 12(5), pages 1-32, May.
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    5. Ebrahimian, Hamed & Ghaffari, Parisa & Ghameshlou, Arezoo N. & Tabatabaei, Sayyed-Hassan & Alizadeh Dizaj, Amin, 2020. "Extensive comparison of various infiltration estimation methods for furrow irrigation under different field conditions," Agricultural Water Management, Elsevier, vol. 230(C).
    6. Samad Emamgholizadeh & Amin Seyedzadeh & Hadi Sanikhani & Eisa Maroufpoor & Gholamhosein Karami, 2022. "Numerical and artificial intelligence models for predicting the water advance in border irrigation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 558-575, January.

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