摘要
引入一种基于改进人工蜂群算法的K均值聚类算法,利用人工蜂群算法优化K均值聚类算法的聚类中心,消除K均值聚类算法因初始聚类中心随机性造成的影响和陷入局部最优解的可能;以金沙江流域下游的寸滩站为研究对象进行洪水分类研究。结果表明,基于改进人工蜂群算法的K均值聚类算法得到的分类结果更加准确合理,并且通过判别分析,进一步说明该算法用于解决洪水分类实际问题的可行性及优越性。
This paper introduced a K-means clustering algorithm based on improved arificial bee colony algorithm,which used arificial bee colony algorithm to optimize the location of the cluster center of K-means clustering algorithm,eliminating the influence caused by the randomness of the initial cluster center and the possibility of falling into the local optimal solution.Flood classification study was conducted at the Cuntan station in the lower basin of the Jinsha River.From the classification results,it can be shown that the results obtained using the K-means Clustering algorithm based on improved arificial bee colony algorithm are more accurate and reasonable,and the discriminant analysis further illustrates the superiority of the algorithm for solving the practical problem of flood classification.
作者
尤烽骅
余玉聪
刘招
刘宇
李雯晴
杨舟
YOU Fenghua;YU Yucong;LIU Zhao;LIU Yu;LI Wenqing;YANG Zhou(School of Water and Environment,Chang,an University,Xi'an 710054,China;Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of Ministry of Education,Chang,an University,Xi,an 710054,China;Institute of Water and Development,Chang,an University,Xi,an 710054,China)
出处
《水文》
CSCD
北大核心
2021年第4期40-47,共8页
Journal of China Hydrology
基金
陕西省自然科学基金联合基金项目(2021JLM-54)
陕西省重点研发计划项目(2019SF-237)
中央高校基本科研业务费资助项目(300102291507,300102269201)
国家111引智计划项目(B08039)。
关键词
人工蜂群算法
K均值聚类算法
洪水分类
金沙江流域
artificial bee colony algorithm
K-means clustering algorithm
flood classification
Jinsha River basin
作者简介
尤烽骅(1997-),男,黑龙江伊春人,硕士研究生,主要研究方向为水文学及水资源。E-mail:yfh329@163.com;通信作者:刘招(1975-),男,陕西礼泉人,教授,主要研究方向为水文学及水资源。E-mail:lz975@163.com。
