Abstract
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.
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Chau, K.W., Cheng, C.T.: Real-time Prediction of Water Stage with Artificial Neural Network Approach. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2557, p. 715. Springer, Heidelberg (2002)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, pp. 1942–1948 (1995)
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proceedings of the 1997 International Conference on Evolutionary Computation, Indianapolis, pp. 303–308 (1997)
Clerc, M., Kennedy, J.: The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Chau, K.W.: River Stage Forecasting with Particle Swarm Optimization. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 1166–1173. Springer, Heidelberg (2004)
Chau, K.W.: Rainfall-Runoff Correlation with Particle Swarm Optimization Algorithm. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 970–975. Springer, Heidelberg (2004)
Slade, W.H., Ressom, H.W., Musavi, M.T., Miller, R.L.: Inversion of Ocean Color Observations using Particle Swarm Optimization. IEEE Transactions on Geoscience and Remote Sensing 42(9), 1915–1923 (2004)
Chau, K.W.: Manipulation of Numerical Coastal Flow and Water Quality Models. Environmental Modelling & Software 18(2), 99–108 (2003)
Chau, K.W.: Intelligent Manipulation of Calibration Parameters in Numerical Modeling. Advances in Environmental Research 8(3-4), 467–476 (2004)
Chau, K.W.: A Three-Dimensional Eutrophication Modeling in Tolo Harbour. Applied Mathematical Modelling 28(9), 849–861 (2004)
Chau, K.W.: Selection and Calibration of Numerical Modeling in Flow and Water Quality. Environmental Modeling and Assessmen 9(3), 169–178 (2004)
Chau, K.W., Chen, W.: A Fifth Generation Numerical Modelling System in Coastal Zone. Applied Mathematical Modelling 25(10), 887–900 (2001)
Chau, K.W., Chen, W.: An Example of Expert System on Numerical Modelling System in Coastal Processes. Advances in Engineering Software 32(9), 695–703 (2001)
Chau, K.W., Cheng, C., Li, C.W.: Knowledge Management System on Flow and Water Quality Modeling. Expert Systems with Applications 22(4), 321–330 (2002)
Chau, K.W., Jin, H.S.: Eutrophication Model for a Coastal Bay in Hong Kong. Journal of Environmental Engineering ASCE 124(7), 628–638 (1998)
Chau, K.W., Jin, H.S., Sin, Y.S.: A Finite Difference Model of Two-Dimensional Tidal Flow in Tolo Harbor, Hong Kong. Applied Mathematical Modelling 20(4), 321–328 (1996)
Jin, H.S., Egashira, S., Chau, K.W.: Carbon to Chlorophyll-a Ratio in Modeling Long-Term Eutrophication Phenomena. Water Science and Technology 38(11), 227–235 (1998)
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Chau, K.W. (2005). Algal Bloom Prediction with Particle Swarm Optimization Algorithm. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_95
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DOI: https://doi.org/10.1007/11596448_95
Publisher Name: Springer, Berlin, Heidelberg
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