江西科学2025，Vol.43Issue(1)：10-17,8.DOI:10.13990/j.issn1001-3679.2025.01.002
基于改进密度峰值聚类的河湖巡查定界算法
An Algorithm for River and Lake Patrol Boundary Based on Improved Density Peak Clustering
摘要
Abstract
The demarcation of rivers and lakes serves as the basis for the inspection scope of river and lake chiefs and is fundamental to river and lake management.In order to address the shortcomings of existing methods for river and lake patrol boundary demarcation,the pa-per proposes an improved density peak clustering algorithm based on the current practices of grassroots river and lake chiefs'patrol work in the province,utilizing historical patrol data.This algorithm calculates relative density based on natural nearest neighbor method under Mahalanobis distance,adopts adaptive clustering center selection and merging strategy,and uses convex hull algorithm to form virtual electronic fence,thereby establishing effective boundaries for river and lake patrol.Comparative experiments with several classic clustering algorithms have shown that this algorithm has excellent clustering performance.Finally,taking the real scene of river and lake patrols in the Fuhe River Basin as an example,the practical application effect of the algorithm on historical river patrol trajectory data is presen-ted,further verifying the feasibility and effectiveness of the algorithm.关键词
巡河轨迹/马氏距离/自然最近邻/密度峰值聚类/越界巡查Key words
river patrol trajectory/Mahalanobis distance/natural nearest neighbor/density peak clustering/cross-boundary patrol分类
信息技术与安全科学引用本文复制引用
谢敏,周毅超,曾斌,罗莹,王鹏,王文丰..基于改进密度峰值聚类的河湖巡查定界算法[J].江西科学,2025,43(1):10-17,8.基金项目
国家自然科学基金项目(61962036) （61962036）
江西省水利厅科技重点研发项目(202325ZDKT17,202426ZDKT13). （202325ZDKT17,202426ZDKT13）
