Nonlinear Prediction of River Water-Stages by Feedback Artificial Neural Network

Kazuaki Hiramatsu, Shiomi Shikasho, Ken Mori

研究成果: ジャーナルへの寄稿記事

4 引用 (Scopus)

抄録

The feedback artificial neural network model (FBANNM) was applied to the prediction of the water-stages in a tidal river. The difference between a feed forward artificial neural network model and a FBANNM was investigated. A simple genetic algorithm (SGA) was then incorporated into a FBANNM to help search for the optimal network structure, especially the unit numbers of an input layer and a hidden layer. It was concluded that the FBANNM was a useful tool in the short-term prediction of the water-stages that had a strong autocorrelation due to tidal motion. The optimal network structure of the FBANNM was effectively determined by the SGA incorporating the fitness defined by Akaike's Information Criterion.

元の言語英語
ページ(範囲)137-147
ページ数11
ジャーナルJournal of the Faculty of Agriculture, Kyushu University
44
発行部数1-2
出版物ステータス出版済み - 11 1 1999

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Neural Networks (Computer)
river water
Rivers
neural networks
prediction
Water
autocorrelation
water
rivers

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Agronomy and Crop Science

これを引用

Nonlinear Prediction of River Water-Stages by Feedback Artificial Neural Network. / Hiramatsu, Kazuaki; Shikasho, Shiomi; Mori, Ken.

:: Journal of the Faculty of Agriculture, Kyushu University, 巻 44, 番号 1-2, 01.11.1999, p. 137-147.

研究成果: ジャーナルへの寄稿記事

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