为提高马斯京根洪水演算模型参数估计的准确性、稳定性和工作效率,根据马斯京根洪水演算模型的基本假定把模型参数估计问题转换为相应的优化问题,并提出用加速遗传算法(AGA)同时优化模型参数。实例计算的结果说明了用AGA进行参数估计的有效性和较高的演算精度,实现了参数估计的优化和简化,在洪水灾害管理中具有推广应用价值。
River flood routing is very important in regional flood disaster management. Now Muskingum flood routing model has widely been applied in river flood routing because of its simple and convenient computation and well applicability. In order to improve accurateness, stability and efficiency of the parameter estimation of Muskingum flood routing model and to facilitate flood forecasting, reservoir flood control operation and flood control planning, the parameter estimation of Muskingum flood routing model was transformed into a nonlinear optimal procession based on the fundamental hypothesis of Muskingum flood routing model in this paper. And an improved genetic algorithm, named accelerating genetic algorithm (AGA) was developed to optimize all of the model parameters of Muskingum flood routing model at the same time. The applied results show that AGA is more effective and high precision for the river flood routing compared with common parameter estimation methods such as try-and-error method, hunting method, and least square method. Due to its capability of realizing the optimization and simplification of the parameter estimation of Muskingum flood routing model, AGA can be widely applied to different complex optimal problems of flood disaster management.
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