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

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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)85-96
Number of pages12
JournalMemoirs of the Faculty of Engineering, Kyushu University
Volume62
Issue number2
Publication statusPublished - Jun 1 2002

Fingerprint

downscaling
Catchments
Rain
river basin
Rivers
artificial neural network
rainfall
Neural networks
Water resources
precipitable water
flood control
prediction
precipitation intensity
Flood control
Drought
wind velocity
drought
weather
engineering
River basin

All Science Journal Classification (ASJC) codes

  • Energy(all)
  • Atmospheric Science
  • Earth and Planetary Sciences(all)
  • Management of Technology and Innovation

Cite this

Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks. / Ishikawa, Izumi; Olsson, Jonas; Jinno, Kenji; Kawamura, Akira; Nishiyama, Koji; Berndtsson, Ronny.

In: Memoirs of the Faculty of Engineering, Kyushu University, Vol. 62, No. 2, 01.06.2002, p. 85-96.

Research output: Contribution to journalArticle

Ishikawa, Izumi ; Olsson, Jonas ; Jinno, Kenji ; Kawamura, Akira ; Nishiyama, Koji ; Berndtsson, Ronny. / Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks. In: Memoirs of the Faculty of Engineering, Kyushu University. 2002 ; Vol. 62, No. 2. pp. 85-96.
@article{24479e75b2e5409b945e48baad40f5cd,
title = "Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks",
abstract = "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.",
author = "Izumi Ishikawa and Jonas Olsson and Kenji Jinno and Akira Kawamura and Koji Nishiyama and Ronny Berndtsson",
year = "2002",
month = "6",
day = "1",
language = "English",
volume = "62",
pages = "85--96",
journal = "Memoirs of the Faculty of Engineering, Kyushu University",
issn = "1345-868X",
publisher = "Kyushu University, Faculty of Science",
number = "2",

}

TY - JOUR

T1 - Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks

AU - Ishikawa, Izumi

AU - Olsson, Jonas

AU - Jinno, Kenji

AU - Kawamura, Akira

AU - Nishiyama, Koji

AU - Berndtsson, Ronny

PY - 2002/6/1

Y1 - 2002/6/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0344099510&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0344099510&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0344099510

VL - 62

SP - 85

EP - 96

JO - Memoirs of the Faculty of Engineering, Kyushu University

JF - Memoirs of the Faculty of Engineering, Kyushu University

SN - 1345-868X

IS - 2

ER -