Long-term prediction of discharges in manwan reservoir using artificial neural network models

Chuntian Cheng, Kwok Wing Chau, Yingguang Sun, Jianyi Lin

Research output: Journal article publicationConference articleAcademic researchpeer-review

139 Citations (Scopus)

Abstract

Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide better accuracy in forecasting river flow than does the auto-regression time series model. In particular, the scaled conjugate gradient algorithm furnishes the highest correlation coefficient and the smallest root mean square error. This ANN model is finally employed in the advanced water resource project of Yunnan Power Group.
Original languageEnglish
Pages (from-to)1040-1045
Number of pages6
JournalLecture Notes in Computer Science
Volume3498
Issue numberIII
Publication statusPublished - 26 Sep 2005
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: 30 May 20051 Jun 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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