Home >

news Help

Publication Information


Title
Japanese:タイ国における事前学習済みのTransformerモデルを用いたダム流入量予測 
English:DAM INFOLOW FORECASTING IN THAILAND USING A PRE-TRAINED TRANSFORMER MODEL 
Author
Japanese: 東儀 奈樹, 梶山 青春, 鼎 信次郎.  
English: Daiki Togi, Kiyoharu Kajiyama, Shinjiro Kanae.  
Language Japanese 
Journal/Book name
Japanese:土木学会論文集 
English:Japanese Journal of JSCE 
Volume, Number, Page 80    16    ID: 23-16148
Published date Feb. 2024 
Publisher
Japanese:公益社団法人 土木学会 
English:Japan Society of Civil Engineers 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL https://www.jstage.jst.go.jp/article/jscejj/80/16/80_23-16148/_article/-char/ja
 
DOI https://doi.org/10.2208/jscejj.23-16148
Abstract  In the event of flooding, damage can be reduced by pre-release water. In Thailand, however, the rainy and dry seasons are separated, and dams need to store enough water for the dry season. This results in a high risk of pre-discharge flood. Therefore, it is important to accurately predict dam inflows to make decisions on pre-releases. In this study, we used the Transformer model, which has been attracting attention recently and is becoming a trend in machine learning. A large amount of data is necessary to improve the accuracy of machine learning, but there is only a limited number of monthly inflow data. This study uses the data from Sirikit Dam for pretraining to predict the monthly inflows at Bhumipol Dam and Srinakarin Dam. As a result, the Nash-Sutcliffe efficiency is successfully improved from 0.17 to 0.75 by using incremental learning when the training period was only 5 years.

©2007 Institute of Science Tokyo All rights reserved.