A split-step PSO algorithm in prediction of water quality pollution

Research output: Journal article publicationConference articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

In order to allow the key stakeholders to have more float time to take appropriate precautionary and preventive measures, an accurate prediction of water quality pollution is very significant. Since a variety of existing water quality models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptions to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong. 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 are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the usual PSO algorithm, it attains a higher accuracy in a much shorter time.
Original languageEnglish
Pages (from-to)1034-1039
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

  • Computer Science (miscellaneous)

Cite this