A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

Aijia Ouyang, Li-Bin Liu, Zhou Sheng and Fan Wu

Mathematical Problems in Engineering, 2015, vol. 2015, 1-15

Abstract:

Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if , but interestingly when , the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:573894

DOI: 10.1155/2015/573894

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