基于免疫粒子群算法的马斯京根模型参数识别

Muskingum Model Parameters Recognition Based on Immune Particle Swarm Algorithm

目前在河道汇流计算中广泛应用的是马斯京根模型,该模型参数的优化求解是影响能否精确模拟实测水文过程的关键问题。以往的模型参数求解大多采用试错法、矩法等方法,计算过程繁琐,计算精度不高,影响到模型的模拟精度。针对此类问题,笔者将免疫原理与粒子群优化算法有机结合,提出了免疫粒子群优化新方法,有效解决了传统方法的计算结果精度不高的问题,并在马斯京根模型参数优化求解中得以应用,结果表明免疫粒子群算法的计算结果精度令人满意,为河道洪水演算方面研究提供了一种新的研究方法和研究模式。

Muskingum model is widely used in the calculation of channel flow concentration,in which the parameters are important in simulating the observed hydrologic process.The traditional methods of optimizing model parameters are mostly trial and error and moment methods which involve complex calculation process and inaccuracy results that affect the precision of model simulating.In order to solve this problem,this paper used an improved immue particle swarm algorithm which can solve the disadvantage of low precision of traditional methods effectively,and was applied in optimizing Muskingum model parameters.The results show that the precision of this method wins great satisfaction and provides a new way in the field of channel flood routing