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

L. V. Chinh, Kazuaki Hiramatsu, Harada Masayoshi, M. Mori

研究成果: ジャーナルへの寄稿記事

9 引用 (Scopus)

抄録

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 1100 ha 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 60 min. 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 60 min. 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 20 min 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.

元の言語英語
ページ(範囲)1332-1338
ページ数7
ジャーナルAgricultural Water Management
96
発行部数9
DOI
出版物ステータス出版済み - 9 1 2009
外部発表Yes

Fingerprint

drainage channels
artificial neural network
neural networks
canal
surface water level
water level
agricultural land
drainage
tides
rain
tide
rainfall
time series analysis
basins
time series
managers
basin
Japan
farmers
irrigation and drainage

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science
  • Water Science and Technology
  • Soil Science
  • Earth-Surface Processes

これを引用

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title = "Estimation of water levels in a main drainage canal in a flat low-lying agricultural area using artificial neural network models",
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 1100 ha 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 60 min. 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 60 min. 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 20 min 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.",
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