TY - GEN
T1 - Algal bloom prediction with particle swarm optimization algorithm
AU - Chau, Kwok Wing
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Precise prediction of algal booms is beneficial to fisheries and environmental management since it enables the fish farmers to gain more ample time to take appropriate precautionary measures. 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. However, in order to accomplish this goal successfully, usual problems and drawbacks in the training with gradient algorithms, i.e., slow convergence and easy entrapment in a local minimum, should be overcome first. This paper presents the application of a particle swarm optimization model for training perceptrons to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong, with different lead times on the basis of several input hydrodynamic and/or water quality variables. It is shown that, when compared with the benchmark backward propagation algorithm, its results can be attained both more accurately and speedily.
AB - Precise prediction of algal booms is beneficial to fisheries and environmental management since it enables the fish farmers to gain more ample time to take appropriate precautionary measures. 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. However, in order to accomplish this goal successfully, usual problems and drawbacks in the training with gradient algorithms, i.e., slow convergence and easy entrapment in a local minimum, should be overcome first. This paper presents the application of a particle swarm optimization model for training perceptrons to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong, with different lead times on the basis of several input hydrodynamic and/or water quality variables. It is shown that, when compared with the benchmark backward propagation algorithm, its results can be attained both more accurately and speedily.
UR - http://www.scopus.com/inward/record.url?scp=33646822486&partnerID=8YFLogxK
U2 - 10.1007/11596448_95
DO - 10.1007/11596448_95
M3 - Conference article published in proceeding or book
SN - 3540308180
SN - 9783540308188
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 645
EP - 650
BT - Computational Intelligence and Security - International Conference, CIS 2005, Proceedings
T2 - International Conference on Computational Intelligence and Security, CIS 2005
Y2 - 15 December 2005 through 19 December 2005
ER -