River stage forecasting with particle swarm optimization

Research output: Journal article publicationConference articleAcademic researchpeer-review

42 Citations (Scopus)

Abstract

An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks, such as very slow convergence and easily getting stuck in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting real-time water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown from the verification simulations that faster and more accurate results can be acquired.
Original languageEnglish
Pages (from-to)1166-1173
Number of pages8
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3029
Publication statusPublished - 9 Dec 2004
Event17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada
Duration: 17 May 200420 May 2004

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science(all)
  • Theoretical Computer Science

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