Application of particle swarm optimization in parameter calibration of channel hydrodynamic model
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Abstract:
Parameter estimation has always been one difficulty in channel hydrodynamic model. Based on the traditional method of calibrating model parameters by personal experience, we proposed a method to optimize and correct model parameters based on the Particle Swarm Optimization algorithm, and established an optimization model for parameter correction. Then we coupled the algorithm with the channel hydrodynamic model. We studied the area comprised of the main Huai River and Shiguan River tributary. Using 1D river flood routing model, we compared the roughness coefficient correction method and the traditional empirical estimation method. Results showed that the corrected roughness coefficient was 0.01 larger on average than the experiential roughness coefficient. The roughness in Huai River was slightly larger than the roughness in Shiguan River tributary. The water level hydrograph simulated by the corrected roughness coefficient fit the measured value better than that by the experiential roughness coefficient. Especially , for the main peak period of the flood hydrograph, the simulation accuracy was improved significantly. Thus, the validity of the proposed algorithm was verified. This algorithm provides an effective method for determining the parameters of complex channel hydrodynamic model.
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