Strahler河流分级方法的算法实现与实证分析  

Algorithm Implementation and Empirical Analysis of Strahler River Classification Method

作　　者：刘辉辉 马乐军 李文峰 陈文君 LIU Hui-hui;MA Le-jun;LI Wen-feng;CHEN Wen-jun(Jinling Institute of Technology,Nanjing 211169,China;不详)

机构地区：[1]金陵科技学院软件工程学院,江苏南京211169 [2]金陵科技学院网络安全学院,江苏南京211169

出　　处：《金陵科技学院学报》2022年第4期10-17,共8页Journal of Jinling Institute of Technology

基　　金：金陵科技学院高层次人才科研启动基金(jit-b-202007);国家自然科学基金青年资助项目(42101476)。

摘　　要：主流的地理信息系统(GIS)通常集成了Strahler河流分级功能,但此方式掩盖了河流分级算法的本质,难以优化其性能,也无法作为独立模块对外提供服务。为此,设计并实现了Strahler河流分级递归(RARC)和非递归(NRARC)两种算法,在真实河网数据集上比较了这两种算法的性能。实验结果表明:河网规模影响分级算法的运行性能,在中等和大规模河网上,RARC算法性能优于NRARC算法;不同框架的存储结构在一定程度上影响了河流分级算法的运行效率。以上结果证明本研究设计的河网分级框架具有较高的可扩展性和可复用性。The mainstream geographic information system(GIS) usually integrates the function of Strahler river classification. However, this method covers up the essence of the river classification algorithm, which is difficult to optimize its performance and cannot provide services as an independent module. For this reason, two algorithms of Strahler recursive algorithm for river classification(RARC) and non-recursive algorithm for river classification(NRARC) are designed and implemented, and their performances are compared on the real river network data sets. The experimental results show that the scale of river network affects the performance of the classification algorithm, and RARC algorithm outperforms NRARC algorithm in medium and large river networks. The storage structures of different frameworks affect the operation efficiency of river classification algorithm to a certain extent. The above results prove that the river network classification framework designed in this study has high scalability and reusability.

关 键 词：河流网络 Strahler河流分级 地理信息系统 递归算法 非递归算法 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]