A split-step particle swarm optimization algorithm in river stage forecasting

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85 Citations (Scopus)

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 at Fo Tan in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging station (Tin Sum) or at Fo Tan. 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)131-135
Number of pages5
JournalJournal of Hydrology
Volume346
Issue number3-4
DOIs
Publication statusPublished - 30 Nov 2007

Keywords

  • Artificial neural networks
  • Levenberg-Marquardt algorithm
  • Particle swarm optimization
  • River stage forecasting
  • Split-step

ASJC Scopus subject areas

  • Soil Science
  • Earth-Surface Processes

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