Journal of Water Resource and Protection

Volume 4, Issue 3 (March 2012)

ISSN Print: 1945-3094   ISSN Online: 1945-3108

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Particle Swarm Optimization for Identifying Rainfall-Runoff Relationships

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DOI: 10.4236/jwarp.2012.43014    4,498 Downloads   7,725 Views  Citations
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ABSTRACT

Rainfall-runoff processes can be considered a single input-output system where the observed rainfall and runoff are inputs and outputs, respectively. Conventional models of these processes cannot simultaneously identify unknown structures of the system and estimate unknown parameters. This study applied a combinational optimization and Particle Swarm Optimization (PSO) for simultaneous identification of system structure and parameters of the rainfall-runoff relationship. Subsystems in proposed model are modeled using combinations of classic models. Classic models are used to transform the system structure identification problem into a combinational optimization and can be selected from those typically used in the hydrological field. A PSO is then applied to select the optimized subsystem model with the best data fit. The parameters are estimated simultaneously. The proposed model is tested in a case study of daily rainfall-runoff for the upstream Kee-Lung River. Comparison of the proposed method with simple linear model (SLM) shows that, in both calibration and validation, the PSO simulates the time of peak arrival more accurately compared to the SLM. Analytical results also confirm that the PSO accurately identifies the system structure and parameters of the rainfall-runoff relationship, which are a useful reference for water resource planning and application.

Share and Cite:

C. Chou, "Particle Swarm Optimization for Identifying Rainfall-Runoff Relationships," Journal of Water Resource and Protection, Vol. 4 No. 3, 2012, pp. 115-126. doi: 10.4236/jwarp.2012.43014.

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[3] Computational Intelligence Techniques and Applications
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[4] Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
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