Abstract
How to exploit current information techniques for rapidly and accurately building a fittest neural network becomes increasingly significant for flood peak forecasting. This paper firstly designs a distributed computing architecture and builds a computing environment based on Grid technologies. Then a distributed computing service for neural networks based on a genetic algorithm and a modified BP algorithm is designed and developed to rapidly and accurately building a fittest neural network for flood peak forecasting. Finally, a distributed computing prototype system is developed and implemented on a case study of the flood prevention in Shenzhen city, China. The experiment result shows that the scheme addressed in the paper is efficient and feasible.
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.
References
Michael, B., Yang, J.: Functional networks in real-time flood forecasting—a novel application. Advances in Water Resources 28, 899–909 (2005)
Holger, R.M., Graeme, C.D.: Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environmental Modeling and Software 15, 101–124 (2000)
Seiffert, U.: Artificial neural networks on massively parallel computer hardware. Neurocomputing 28, 135–150 (2004)
Ian, F., Carl, K., Steven, T.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations (2001), http://www.globus.org/alliance/publications/papers/anatomy.pdf
Ian, F., Carl, K., Jeffrey, M.N., Steven, T.: The physiology of the grid: An open grid services architecture for distributed system integration. Technical report (2002), http://www.globus.org/alliance/publications/papers/ogsa.pdf
Li, H., Liu, H.: Peak value recognition theory of artificial neural network and its application to flood forecasting. SHUILI XUEBAO 6, 15–20 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, J., Liu, C., Gong, J., Wang, D., Song, T. (2006). A Distributed Computing Service for Neural Networks and Its Application to Flood Peak Forecasting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_98
Download citation
DOI: https://doi.org/10.1007/11893257_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)