Nonlinear forecasting of daily inflow using neural network

Hyun Suk Shin, Tae Woong Kim, Joong Hoon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The daily inflow has one of apparent nonlinear and complicated phenomena. The nonlinearity and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear scheme. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the Back propagation algorithm which is one of neural network structures is modified by combining the regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using regression scheme in BP algorithm is showed in the low and high inflows.

Original languageEnglish
Title of host publicationWRPMD 1999: Preparing for the 21st Century
PublisherAmerican Society of Civil Engineers (ASCE)
ISBN (Print)0784404305, 9780784404300
Publication statusPublished - 1999 Jan 1
Event29th Annual Water Resources Planning and Management Conference, WRPMD 1999 - Tempe, AZ, United States
Duration: 1999 Jun 61999 Jun 9


Other29th Annual Water Resources Planning and Management Conference, WRPMD 1999
Country/TerritoryUnited States
CityTempe, AZ

ASJC Scopus subject areas

  • Computer Networks and Communications


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