レーダデータを用いたニューラルネットワークによる発電用ダム上流域の地上雨量分布推定

Material type: 文書・図像類

Author: 水野, 勝教ほか

Publisher: 愛知工業大学

Publication date: 1996-03-31

Material Format: Digital

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Note (General)：: This paper describes an application of a neural network method for estimating the ground rainfall distribution from radar echo data. A neural network ...

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Material Type: 文書・図像類
Title: レーダデータを用いたニューラルネットワークによる発電用ダム上流域の地上雨量分布推定
Author/Editor: 水野, 勝教
後藤, 泰之
一柳, 勝宏
Author Heading: 水野, 勝教
後藤, 泰之
一柳, 勝宏
Publication, Distribution, etc.: 愛知工業大学
Publication Date: 1996-03-31
Publication Date (W3CDTF): 1996-03-31
Alternative Title: レーダデータオモチイタニューラルネットワークニヨルハツデンヨウダムジョウリュウイキノチジョウウリョウブンプスイテイ
An Estimation of Ground Rainfall Distribution on Upper District of a Hydro-Power Plant Dam by Using Artificial Neural Network Taking Account of Radar Echo Data
Periodical title: 愛知工業大学研究報告. B, 専門関係論文集 = Bulletin of Aichi Institute of Technology. Part B
No. or year of volume/issue: 31
Volume: 31
Pages: 1-6
ISSN (Periodical Title): ISSN : 0387-0812
Text Language Code: jpn
Target Audience: 一般
Note (General): This paper describes an application of a neural network method for estimating the ground rainfall distribution from radar echo data. A neural network system for this purpose is developed through a case study on a dam for hydro-power plant located the upper district of the Hida River in Central Japan. We use the neural network comprised of three layers; an input layer, a hidden layer and an output layer. The input data to the neural network are a radar echo amount observed in each radar mesh and x-y coordinates showing its location. The output from the neural network is the estimated ground rainfall amount. Thus, the neural network has three nodes for the input layer and a single node for the output layer. A set of three nodes is adopted for the hidden layer. It is found that the estimating system yields goods results for the ground rainfall distribution. Further, the height of the ground corresponded with to the radar mesh is taken as an additional input datum. It is also found from our investigations that the estimating accuracy of the neural network is improved by the additional datum.
identifier:http://repository.aitech.ac.jp/dspace/handle/11133/997
Access Restrictions: 限定公開
Periodical Title (NCID): AN00007801
https://ci.nii.ac.jp/ncid/AN00007801
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 愛知工業大学 : 愛知工業大学学術情報リポジトリ