摘要
开展云南省山洪灾害危险性区划工作,以自组织映射神经网络为基础,混合Ward、PAM、CLARA、K-means和HK-means的5种方法进行二阶聚类,应用戴维森堡丁指数(Davies-Bouldin index,DBI)、轮廓系数(silhouette coefficient,SC)、聚类模型评估指数(Calinski-Harabaz index,CH)确定最佳聚类方案,之后以变异系数和变异系数一阶拆分确定最佳区划数量.结果显示:①SOM(self organizing map)+CLARA(clustering LARge applications)方法通过聚类有效性检验效果最好,其DBI值为1.0、SC值为0.9、CH值为0.3334,基于该方法得到云南省山洪灾害危险性最佳聚类数为5类,呈现类别空间分离,灾害属性相似的特征;②通过变异系数(coefficient of variation,CV)值变化及变异系数一阶差分(first-order difference,FOD)最低取值确定云南省山洪灾害危险性最佳区划单元为16个,具有形状上与地貌单元相近、数量上与行政单元相同,内部灾害发生机理相似的特征;③通过山洪灾害点、降水量、高程地貌的可视化比较,地理探测器定量分析,表明区划结果有较高的区内一致性和区间异质性.
The flash flood hazard zoning is conducted to prevent flood disasters and manage their risks effectively in Yunnan Province.Herein,we employed a self-organizing mapping method to conduct second-order clustering,utilizing five distinct techniques,namely,Ward,PAM,CLARA,K-means,and HK-means.The evaluation of clustering schemes was performed,using the Davies-Bouldin index(DBI),Silhouette coefficient(SC),and Calinski-Harabaz index(CH).Subsequently,the optimal number of zoning units was determined through the utilization of the coefficient of variation and the first-order difference of variance coefficients.The results reveal that:①The self organizing map(SOM)+clustering LARge applications(CLARA)method demonstrates best effectiveness according to the clustering validity test,with a DBI value of 1.0,SC value of 0.9,and CH value of 0.3334.The optimal number of clusters for flash flood hazards in Yunnan Province is determined to be 5,displaying characteristics of spatial separation among categories and similarity in hazard attributes.②By analyzing the variation of coefficient of variation(CV)value and identifying the lowest first-order difference(FOD)value,the optimal zoning units are found out to be 16.These zoning units exhibit the characteristics of their forms similar to geomorphological units,of their numbers equal to administrative units,and of similar internaldisaster-occurrence mechanisms.③Through visualizing comparison of flash flood sites,precipitation and elevation geomorphology,geodetector quantitative analysis,the result shows high intra-zoning consistency and high inter-zoning heterogeneity within the 16 zones.
作者
高耀
陈俊旭
徐佳
吕丽花
梁宗玲
赵璐沅
王子尧
GAO Yao;CHEN Junxu;XU Jia;LYU Lihua;LIANG Zongling;ZHAO Luyuan;WANG Ziyao(School of Earth Sciences,Yunnan University,Kunming 650500,Yunnan,China;International Joint Research Center for Karstology,Yunnan University,Kunming 650500,Yunnan,China;Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring,Kunming 650500,Yunnan,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第6期1067-1077,共11页
Journal of Yunnan University(Natural Sciences Edition)
基金
云南省科技厅基础研究计划(202301BF070001-004)
云南省研究生优质课程立项建设项目(SJYZKC20211110)
云南大学研究生科研创新基金(KC-23235433).
关键词
区划
山洪灾害危险性
两阶段混合聚类
自组织映射神经网络
云南省
regionalization
flash flood hazard
two-stage hybrid clustering
self-organizing mapping neural networks
Yunnan Province
