Abstract
A reliable correlation between rainfall-runoff enables the local authority to gain more amble time for formulation of appropriate decision making, issuance of an advanced flood forewarning, and execution of earlier evacuation measures. Since a variety of existing methods such as rainfall-runoff modeling or statistical techniques involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution, provided that their drawbacks can be overcome. Usual problems in the training with gradient algorithms are the slow convergence and easy entrapment in a local minimum. This paper presents a particle swarm optimization model for training perceptrons. It is applied to forecasting real-time runoffs in Siu Lek Yuen of Hong Kong with different lead times on the basis of the upstream gauging stations or at the specific station. It is demonstrated that the results are both more accurate and faster to attain, when compared with the benchmark backward propagation algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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, pp. 715–715. 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.: 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 Prototype Knowledge-Based System on Unsteady Open Channel Flow in Water Resources Management. Water International 29(1), 54–60 (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., Lee, J.H.W.: Mathematical Modelling of Shing Mun River Network. Advances in Water Resources 14(3), 106–112 (1991)
Chau, K.W., Yang, W.W.: Development of an Integrated Expert System for Fluvial Hydrodynamics. Advances in Engineering Software 17(3), 165–172 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chau, K. (2004). Rainfall-Runoff Correlation with Particle Swarm Optimization Algorithm. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_154
Download citation
DOI: https://doi.org/10.1007/978-3-540-28648-6_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
eBook Packages: Springer Book Archive