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Optimal water allocation in irrigation networks based on real time climatic data

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  • parsinejad, Masoud
  • Yazdi, Amin Bemani
  • Araghinejad, Shahab
  • Nejadhashemi, A. Pouyan
  • Tabrizi, Mahdi Sarai

Abstract

The main objective of this study is to improve allocation of water using real time climatic data to estimate irrigation requirement. A study was conducted on an irrigation network in Northwest of Iran to compare present water allocation technique, calculated based on traditional practice of using long-term averaged climatic data, and proposed practice of using real time data with the actual water allocation determined based on specified season's climatic data. In this study, neural network techniques were used to estimate reference evapotranspiration (ETo), actual evapotrasipiration (ETc), and water allocation requirements. For predicting actual evapotranspiration in the subsequent 10-day period, ETo data for one, two, three previous 10-day periods were used. The results of two different neural network techniques were analyzed and compared separately with season specified and long-term averaged ETc. In regard to ETc prediction, the results showed that focused time-delay method is more efficient than feed-forward, both in 10-day period and in monthly scales. In addition, better estimation can be obtained if climatic data from three preceding 10-day periods are used. Overall, incorporating new techniques resulted in 10–25 percent savings on water allocation within the network.

Suggested Citation

  • parsinejad, Masoud & Yazdi, Amin Bemani & Araghinejad, Shahab & Nejadhashemi, A. Pouyan & Tabrizi, Mahdi Sarai, 2013. "Optimal water allocation in irrigation networks based on real time climatic data," Agricultural Water Management, Elsevier, vol. 117(C), pages 1-8.
    • Handle: RePEc:eee:agiwat:v:117:y:2013:i:c:p:1-8
    DOI: 10.1016/j.agwat.2012.10.025
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    References listed on IDEAS

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    Cited by:

    1. Kropp, Ian & Nejadhashemi, A. Pouyan & Deb, Kalyanmoy & Abouali, Mohammad & Roy, Proteek C. & Adhikari, Umesh & Hoogenboom, Gerrit, 2019. "A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification," Agricultural Systems, Elsevier, vol. 173(C), pages 289-302.
    2. Zhang, Chenglong & Guo, Ping, 2018. "FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation," Agricultural Water Management, Elsevier, vol. 199(C), pages 105-119.
    3. Papagera, A. & Ioannou, K. & Zaimes, G. & Iakovoglou, V. & Simeonidou, M., 2014. "Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-11, December.
    4. Lalehzari, Reza & Kerachian, Reza, 2020. "Developing a framework for daily common pool groundwater allocation to demands in agricultural regions," Agricultural Water Management, Elsevier, vol. 241(C).

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