Abstract
Flood routing is a methodology to predict the changes of the flow of water as it moves through a natural river, an artificial channel, or a reservoir. It is widely used in fields such as flood prediction, reservoir design, geographic planning, and many others. One of the most popular and widely used flood routing techniques is the Muskingum model, as it is conceptually simple and only depends on a few parameters that can be estimated from historical inflow/outflow records. However, the estimation of such parameters for the nonlinear case is still a challenging task. In this paper we present a method based on a powerful swarm intelligence technique called bat algorithm to solve the parameter estimation problem of the nonlinear Muskingum model for channel routing. The method is applied to an illustrative example used as a benchmark in the field with very good results. We also show that our method outperforms other state-of-the-art methods in the field such as PSO.
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Acknowledgements
The research in this paper has been supported by the project PDE-GIR of the EU Horizon 2020 research and innovation program, Marie Sklodowska-Curie grant agreement No 778035; the project #TIN2017-89275-R of the Agencia Estatal de Investigación (Spanish Ministry of Science, Innovation and Universities) and EU Funds EFRD (AEI/FEDER-UE); and the project #JU12, of SODERCAN and EU Funds EFRD (SODERCAN /FEDER-UE). We also thank the reviewers for their insightful comments and suggestions.
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Sánchez, R., Suárez, P., Gálvez, A., Iglesias, A. (2019). Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with the Bat Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_32
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DOI: https://doi.org/10.1007/978-3-030-26369-0_32
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