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Irrigation Science

, Volume 23, Issue 1, pp 29–37 | Cite as

Simulation of nitrate distribution under drip irrigation using artificial neural networks

  • Jiusheng Li
  • R. E. Yoder
  • L. O. Odhiambo
  • J. Zhang
Original Paper

Abstract

Accurate knowledge of nitrate distribution in the soil under fertigation through drip-irrigation systems is fundamentally important for system design and management. The determination of nitrate distribution through modeling represents a highly complex nonlinear problem that includes adsorption, transformation, convection, and dispersion. For this reason, an alternative methodology is proposed, which combines artificial neural networks (ANN) and laboratory experiments. Seventeen experiments with apparent discharge rates varying from 0.6 to 7.8 l/h, the apparent cylindrical applied volume from 6 to 15 l, and the input concentration from 100 to 700 mg/l were conducted to provide a database for establishing the ANN architecture. The model input parameters were initial soil water content, initial nitrate concentration in the soil, discharge rate, input concentration of fertilizer (NH4NO3), applied volume, and final soil water content. The model output was nitrate concentration in the soil after fertigation. A total of 298 vectors were used to train the ANN model, and 212 independent vectors were used to test the model. Results of the test show a good correspondence with a determination coefficient (r 2) of 0.83 between the model-estimated nitrate concentration in the soil and laboratory-measured nitrate concentration in the soil. These results show that the optimized ANN models are reasonably accurate and can provide an easy and efficient means of estimating nitrate distribution in the soil under fertigation through drip-irrigation systems.

Keywords

Artificial Neural Network Soil Water Content Artificial Neural Network Model Drip Irrigation Learning Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (NSFC) (grant no. 59979027), the National High Technology Development Plan Project (grant nos. 2001AA242032, 2002AA2Z40215), and the Tennessee Agricultural Experiment Station.

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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Jiusheng Li
    • 1
  • R. E. Yoder
    • 2
  • L. O. Odhiambo
    • 2
  • J. Zhang
    • 3
  1. 1.Department of Irrigation and DrainageChina Institute of Water Resources and Hydro-Power ResearchBeijingChina
  2. 2.Biosystems Engineering and Environmental Science DepartmentThe University of TennesseeKnoxvilleUSA
  3. 3.Soil and Fertilizer InstituteChinese Academy of Agricultural SciencesBeijingChina

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