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Profile photo of Ronny Berndtsson

Ronny Berndtsson

Professor, Dep Director, MECW Dep Scientific Coordinator

Profile photo of Ronny Berndtsson

Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks

Author

  • Izumi Ishikawa
  • Jonas Olsson
  • Kenji Jinno
  • Akira Kawamura
  • Koji Nishiyama
  • Ronny Berndtsson

Summary, in English

For the proper water resources management of the Chikugo River basin, the prediction of both drought and heavy rainfall needs to be carried out by the conventional and engineering method which can be useful to for the practitioners who work on the water resources management and flood control. A relatively simple and efficient way to estimate local and regional rainfall, as well as other hydrometeorological variables, is now intensively discussed. This method utilizes the grid data point value (GPV) to predict the regional rainfall based on the so called atmospheric downscaling. In this paper, artificial neural networks (ANNs) are employed. As the input variables, three large-scale meteorological variables, precipitable water, and zonal and meridional wind speeds, are used. Output is the mean rainfall intensity in the Chikugo River basin during a 12-hour period. In the model, the serially combined ANNs were employed to predict the rainfall amount exactly. The result from the serially combined ANNs is slightly better than the result from the neumerical weather prediction model of the Japan Meteorological Agency by comparing the values of CC and RMSE.

Department/s

  • Division of Water Resources Engineering

Publishing year

2002-06

Language

English

Pages

85-96

Publication/Series

Memoirs of the Faculty of Engineering, Kyushu University

Volume

62

Issue

2

Document type

Journal article

Publisher

Kyushu University, Faculty of Science

Topic

  • Water Engineering

Keywords

  • Artificial neural network
  • Atmospheric downscaling
  • Correlation analysis
  • GPV data
  • Precipitable water
  • Wind speeds

Status

Published

ISBN/ISSN/Other

  • ISSN: 1345-868X