基于LBA-PP模型的年径流丰枯分类

Wet-Dry Classification of Annual Runoff Based on LBA-PP Model

针对年径流丰枯特性同时取决于径流本身大小和年内时程分配的特点,利用一种基于Lévy飞行策略改进的蝙蝠算法(Lévy Bat Algorithm,LBA)搜索投影寻踪模型(Projection Pursuit,PP)最佳投影方向a,提出LBA-PP年径流丰枯分类模型,并构建粒子群优化(Particle Swarm Optimization,PSO)算法-PP模型,与LBA-PP年径流丰枯分类模型对比,以云南省西洋站为例进行实例研究。结果表明:LBA算法寻优能力优于PSO算法,具有较高的收敛精度、较好的稳健性能和全局寻优能力。利用LBA算法寻优PP模型最佳投影方向a,不但提高了PP模型的分类精度,而且为PP模型最佳投影方向的选取提供了新的途径和方法。LBA-PP模型同时考虑了年径流大小及年内时程分配信息,其分类结果较常规方法更科学、客观。

The wet-dry features of annual runoff depend on the size and time-history distribution characteristics of runoff itself. In view of this,we put forward a LBA-PP model of wet-dry classification of annual runoff by searchingthe optimum projection direction using bat algorithm （LBA） improved with a Levy flight strategy in association with projection pursuit （PP） model. We also construct a particle swarm optimization （PSO） algorithm PP model for comparison,with the annual runoff at Xiyang station in Yunnan Province as a case study. Results show that the LBA algorithm is superior to PSO algorithm,and is of good convergence accuracy, robust performance and global optimization ability. Using LBA algorithm to find the best projection direction of PP model not only improves the classification accuracy of the PP model,but also provides a new way and method for the selection of the PP model. In the LBA-PP model,the annual runoff is considered, and the time history information is distributed. The classification results are more scientific and objective than those of conventional method.