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.
|Number of pages||6|
|Journal||Lecture Notes in Computer Science|
|Publication status||Published - 26 Sep 2005|
|Event||Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China|
Duration: 30 May 2005 → 1 Jun 2005
ASJC Scopus subject areas
- Computer Science (miscellaneous)