Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks
Ishikawa, Izumi ; Olsson, Jonas LU ; Jinno, Kenji ; Kawamura, Akira ; Nishiyama, Koji and Berndtsson, Ronny LU
(2002)
In Memoirs of the Faculty of Engineering, Kyushu University
62(2).
p.85-96
- 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... (More)
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.
(Less)
- author
- Ishikawa, Izumi
; Olsson, Jonas
LU
; Jinno, Kenji
; Kawamura, Akira
; Nishiyama, Koji
and Berndtsson, Ronny
LU
- organization
- publishing date
- 2002-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial neural network, Atmospheric downscaling, Correlation analysis, GPV data, Precipitable water, Wind speeds
- in
- Memoirs of the Faculty of Engineering, Kyushu University
- volume
- 62
- issue
- 2
- pages
- 12 pages
- publisher
- Kyushu University, Faculty of Science
- external identifiers
-
- scopus:0344099510
- ISSN
- 1345-868X
- language
- English
- LU publication?
- yes
- id
- 9ac39065-211c-488f-a3aa-30c55c00f3c8
- date added to LUP
- 2023-08-17 15:11:04
- date last changed
- 2023-08-18 02:48:42
@article{9ac39065-211c-488f-a3aa-30c55c00f3c8,
abstract = {{<p>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.</p>}},
author = {{Ishikawa, Izumi and Olsson, Jonas and Jinno, Kenji and Kawamura, Akira and Nishiyama, Koji and Berndtsson, Ronny}},
issn = {{1345-868X}},
keywords = {{Artificial neural network; Atmospheric downscaling; Correlation analysis; GPV data; Precipitable water; Wind speeds}},
language = {{eng}},
number = {{2}},
pages = {{85--96}},
publisher = {{Kyushu University, Faculty of Science}},
series = {{Memoirs of the Faculty of Engineering, Kyushu University}},
title = {{Rainfall estimation in the Chikugo River Basin by Atmospheric downscaling using artificial networks}},
volume = {{62}},
year = {{2002}},
}