A hybrid algorithm in river stage forecasting

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

An accurate forecast of river stage is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures as required. Since a variety of existing process-based hydrological models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. In this paper, a split-step particle swarm optimization (PSO) model is developed and applied to train multi-layer perceptrons for forecasting 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. This paradigm is able to combine the advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step. The results demonstrate that it is able to attain a higher accuracy in a much shorter time when compared with the benchmarking backward propagation algorithm as well as the standard PSO algorithm.
Original languageEnglish
Pages (from-to)402-405
Number of pages4
JournalDynamics of continuous, discrete & impulsive systems. Series B, Applications & algorithms
Volume13E
Publication statusPublished - 2006
EventInternational Conference on Sensing, Computing and Automation -
Duration: 1 Jan 2006 → …

ASJC Scopus subject areas

  • Applied Mathematics
  • Discrete Mathematics and Combinatorics

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