Home >

news Help

Publication Information


Title
Japanese: 
English:Rainfall Prediction with Global Climate Big Data in Chaophraya River Basin 
Author
Japanese: 長谷川 青春, 鼎 信次郎.  
English: Kiyoharu HASEGAWA, Shinjiro KANAE.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date Aug. 2021 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English:AOGS2021 18th Annual Meeting 
Conference site
Japanese:オンライン 
English:Online 
Official URL https://www.asiaoceania.org/aogs2021/public.asp?page=program_overview.asp
 
Abstract Subseasonal to seasonal (S2S) prediction still remains as a challenge task even today. S2S prediction refers to a lead time ranging from one to several months, which is known to result in rapidly decreasing forecast accuracy. Deep learning (DL), one of the most powerful statistical models, is expected to overcome current difficulty of S2S prediction because of its spatio-temporal locality. In this study, one-month rainfall prediction is carried out for the Chao Phraya River basin in Thailand during the rainy season from May to October, where the introduction of long-term rainfall prediction to dam management is urgently required for flood mitigation. The DL model is constructed using global maps of sea surface temperature and heat content as input values. 17 climate models output from CMIP5 dataset is used to expand the training data from 65 existing observations to 2500, which enables us to train the DL model proper. The prediction accuracy of the DL model is compared with that of the physical model and the linear regression model. The results show that the DL model is slightly inferior to the physical model, but has higher prediction accuracy than the linear model. In addition, the deep learning model has the highest prediction accuracy when trained only on the CMIP5 dataset, indicating that the DL model has potential to outperform the physical model in the future.
Award AOGS2021 Best Student Poster

©2007 Institute of Science Tokyo All rights reserved.