GPU-based PQGA algorithm for estimating parameters of Muskingum model

Accurate parameter estimation for the Muskingum method is important in its use for forecasting flood damage. Combining ideas from quantum computing and evolutionary computing, a quantum genetic algorithm (QGA) is presented for this purpose, which is shown to offer higher precision and greater robustness than eight typical methods. However, it requires longer computation time. In this paper, we therefore provide an implementation of a parallel quantum genetic algorithm (PQGA) in CCUDA for estimating the parameters of the Muskingum model. PQGA was tested on a classic river example from China. Experimental results show that the PQGA scales well with available computing resources and is substantially faster than QGA and the other methods.

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