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Estimation of water levels in a main drainage canal in a flat low-lying agricultural area using artificial neural network models

Author

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  • Chinh, L.V.
  • Hiramatsu, K.
  • Harada, M.
  • Mori, M.

Abstract

The Chiyoda basin is located in the Saga Prefecture of the Kyushu Island, Japan, and lies next to the tidal compartment of the Chikugo River, into which excess water in the basin is drained away. This basin has a total area of approximately 1100ha and is a typical flat and low-lying agricultural area. The estimation of the water levels at the gates and along the main drainage canal is a crucial issue that has recently been the subject of much research. At these locations farmers and managers need to control the operation of the irrigation and drainage systems during periods of cultivation. An attempt has been made to apply a feed-forward artificial neural network (FFANN) to model and estimate the water levels in the main drainage canal. The study indicated that the artificial neural network (ANN) could successfully model the complex relationship between rainfall and water levels in this flat and low-lying agricultural area. Input variables and the model structure were selected and optimized by trial and error, and the accuracy of the model was then evaluated by comparing the simulated water levels with the observed ones during an irrigation period in July 2007. The water levels at two locations, located upstream and downstream of a main drainage canal, were investigated by using a time series at intervals of 20, 30, and 60min. At these intervals, rainfall and tide water levels in the Chikugo River were measured, and the backward time-step numbers of the input variables of rainfall and tide water level were searched. For the upstream location, the optimal combination yielding good agreement between the observed and estimated water levels was obtained when the interval of the time series was 60min. The number of backward time-steps of the input variables of rainfall and tide water level were 5 and 4, respectively. In contrast to the downstream location, the optimal combination was obtained for the interval time series of 20min with 4 backward time-steps for both the input variables of rainfall and tide water level. The present study could provide farmers and managers with a useful tool for controlling water distribution in the drainage basin, and reduce the cost of installing water level observation points at many locations in the main drainage canal.

Suggested Citation

  • Chinh, L.V. & Hiramatsu, K. & Harada, M. & Mori, M., 2009. "Estimation of water levels in a main drainage canal in a flat low-lying agricultural area using artificial neural network models," Agricultural Water Management, Elsevier, vol. 96(9), pages 1332-1338, September.
    • Handle: RePEc:eee:agiwat:v:96:y:2009:i:9:p:1332-1338
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    References listed on IDEAS

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    1. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
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    Cited by:

    1. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.

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