The basic ideas in neural networks
Communications of the ACM
Real-Time Prediction of Water Stage with Artificial Neural Network Approach
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
River stage forecasting with particle swarm optimization
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
An empirical study on sea water quality prediction
Knowledge-Based Systems
Greedy regression ensemble selection: Theory and an application to water quality prediction
Information Sciences: an International Journal
Applying adaptive prediction to sea-water quality measurements
Expert Systems with Applications: An International Journal
Evolutionary extreme learning machine – based on particle swarm optimization
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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 perceptrons 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.