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Title
Japanese: 
English:MONTHLY RESERVOIR INFLOW FORECASTING IN THAILAND: A COMPARISON OF ANN-BASED AND HISTORICAL ANALOUGE-BASED METHODS 
Author
Japanese: Somchit AMNATSAN, 井芹慶彦, 柳川亜季, 吉川沙耶花, 柿沼薫, 鼎信次郎.  
English: Somchit AMNATSAN, Yoshihiko Iseri, Aki Yanagawa, Sayaka Yoshikawa, Kaoru Kakinuma, Shinjiro Kanae.  
Language English 
Journal/Book name
Japanese:土木学会論文集B1（水工学） 
English: 
Volume, Number, Page 72    4    I7-I12
Published date 2016 
Publisher
Japanese:公益社団法人 土木学会 
English: 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
DOI http://doi.org/10.2208/jscejhe.72.I_7
Abstract Accurate forecasting of reservoir inflow is essential for effective reservoir management. In this study, artificial neural network (ANN)-based models and forecasting methods based on historical inflow analogues were used to forecast the monthly reservoir inflows of Sirikit Dam in the Nan River Basin of Thailand. Incorporating sea surface temperatures and ocean indices in the ANN model significantly improved the forecasting result. The wavelet decomposition of inputs before they were fed into the ANN model also improved the forecasting result. The variation analogue forecast produced the best result among the forecasting methods investigated, based on historical analogues. It was also superior to other forecasting methods when forecasting extreme inflow values.

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