Neuro Rainfall Forecast with Data Mining by Real-Coded Genetical Preprocessing
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- Ito Seiji
- Faculty of Engineering, University of Tokushima
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- Mitsukura Yasue
- Faculty of Engineering, University of Tokushima
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- Fukumi Minoru
- Faculty of Engineering, University of Tokushima
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- Akamatsu Norio
- Faculty of Engineering, University of Tokushima
Bibliographic Information
- Other Title
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- 実数値ＧＡを用いたデータマイニングによるニューロ降雨予測システムの設計
- ジッスウチ GA オ モチイタ データマイニング ニ ヨル ニューロ コウウ ヨソク システム ノ セッケイ
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Abstract
In this paper, rainfall is predicted by using a Neural Network(NN) and a Genetic Algorithm(GA). GA selects data needed to predict the rainfall. NN learns and predicts it using attributes selected by GA. The real-coded GA is used to decide data priority, and data really needed for the rainfall forecast are selected based on the priority. In order to show the effectiveness of the proposed rainfall prediction system, computer simulations are performed for real weather data. Finally, the effectiveness of this system is shown with data analysis.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 123 (4), 817-822, 2003
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390282679581018752
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- NII Article ID
- 130000089323
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 6530601
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed
