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Paper Abstract and Keywords
Presentation 2013-10-18 13:55
A Neural Network Model for Forecasting Precipitation Extreme
Junaida Sulaiman, Darwis Herdianti, Hideo Hirose (Kyushu Inst. of Tech.) R2013-65
Abstract (in Japanese) (See Japanese page) 
(in English) Several days of precipitation can increase the magnitude of accumulated water in a basin. This can cause the lower area of community and housing over flooded with rainfall water in a short time. Many researchers are using precipitation data for forecasting the number of rainy days in daily, monthly and yearly. However, with a maximum 5-day precipitation, we can predict the magnitude of precipitation within a specified period for example in a month, that may identified as precipitation extremes. Therefore, this study describes a method to forecast the trend of maximum 5-day precipitation in the following month using a hybrid of artificial neural networks (ANN) and particle swarm optimization (PSO). It is important to analyze the trend of extreme precipitation for future prediction of high precipitations events in the area of interest. ANN is widely applied in the hydrology field due to its non-linearity ability to map a non-stationary and seasonal data. Here, we have compared ANN with seasonal autoregressive integrated moving average (ARIMA) to measure their performances in forecasting next month maximum 5-day precipitation. Prior to model development in ANN, the significant input lags are determined using linear correlation analysis (LCA) and stepwise regression method (SLR), respectively. Results showed that ANN method is feasible in forecasting precipitation extremes when it is trained with the particle swarm optimization.
Keyword (in Japanese) (See Japanese page) 
(in English) artificial neural networks / particle swarm optimization / extreme precipitation / seasonal autoregressive integrated moving average / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 249, R2013-65, pp. 7-12, Oct. 2013.
Paper # R2013-65 
Date of Issue 2013-10-11 (R) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF R2013-65

Conference Information
Committee R  
Conference Date 2013-10-18 - 2013-10-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To R 
Conference Code 2013-10-R 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Neural Network Model for Forecasting Precipitation Extreme 
Sub Title (in English)  
Keyword(1) artificial neural networks  
Keyword(2) particle swarm optimization  
Keyword(3) extreme precipitation  
Keyword(4) seasonal autoregressive integrated moving average  
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1st Author's Name Junaida Sulaiman  
1st Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
2nd Author's Name Darwis Herdianti  
2nd Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
3rd Author's Name Hideo Hirose  
3rd Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
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Speaker Author-1 
Date Time 2013-10-18 13:55:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2013-65 
Volume (vol) vol.113 
Number (no) no.249 
Page pp.7-12 
#Pages 6 
Date of Issue 2013-10-11 (R) 

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