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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. Holzapfel, E. A. & Jara, J. & Zuniga, C. & Marino, M. A. & Paredes, J. & Billib, M., 2004. "Infiltration parameters for furrow irrigation," Agricultural Water Management, Elsevier, vol. 68(1), pages 19-32, July.
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    1. 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.
    2. 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.
    3. 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.
    4. Al-Ghobari, Hussein M. & El-Marazky, Mohamed S. & Dewidar, Ahmed Z. & Mattar, Mohamed A., 2018. "Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques," Agricultural Water Management, Elsevier, vol. 195(C), pages 211-221.
    5. González Perea, R. & Camacho Poyato, E. & Montesinos, P. & Rodríguez Díaz, J.A., 2018. "Prediction of applied irrigation depths at farm level using artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 206(C), pages 229-240.
    6. 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).

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