Defining Homogeneous Regions for Streamflow Processes in Turkey Using a K-Means Clustering Method

被引:29
|
作者
Dikbas, Fatih [1 ]
Firat, Mahmut [2 ]
Koc, A. Cem [1 ]
Gungor, Mahmud [1 ]
机构
[1] Pamukkale Univ, Dept Civil Engn, Fac Engn, Denizli, Turkey
[2] Inonu Univ, Dept Civil Engn, Fac Engn, Malatya, Turkey
基金
美国国家科学基金会;
关键词
Cluster analysis; K-Means; Annual maximum flow; Homogeneous region; FLOOD FREQUENCY-ANALYSIS; DRAINAGE BASINS; CLASSIFICATION;
D O I
10.1007/s13369-013-0542-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The major problem in ungauged basins for planning and management of water resources projects is to estimate the flood magnitudes and frequencies. The identification of hydrologically homogeneous regions is one of the most important steps of regional frequency analysis. In this study, K-Means clustering method is applied to classify the maximum annual flows and identify the hydrologically homogeneous groups. For this aim, the annual maximum river flows, coefficient of variation and skewness of annual maximum river flows, latitude and longitude at 117 stations operated by the General Directorate of Electrical Power Resources Survey and Development Administration throughout Turkey are used. The optimal number of groups was determined as seven. Regional homogeneity test based on L-moments method is applied to check homogeneity of these seven regions identified by clustering analysis. The results show that regions defined by K-Means method can be used for regional flood frequency analysis. According to the results, K-Means method is recommended to identify the hydrologically homogeneous regions for regional frequency analysis.
引用
收藏
页码:1313 / 1319
页数:7
相关论文
共 50 条
  • [21] A Study on Functional Planning in Shanghai Regions Using K-means and DBSCAN Clustering
    Luo, Tianhang
    Ebrahimpour, Zeinab
    Wan, Wanggen
    Cervantes, Ofelia
    2018 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), 2018, : 251 - 256
  • [22] Statistical initialization of intrinsic K-means clustering on homogeneous manifolds
    Tan, Chao
    Zhao, Huan
    Ding, Han
    APPLIED INTELLIGENCE, 2023, 53 (05) : 4959 - 4978
  • [23] Statistical initialization of intrinsic K-means clustering on homogeneous manifolds
    Chao Tan
    Huan Zhao
    Han Ding
    Applied Intelligence, 2023, 53 : 4959 - 4978
  • [24] Robust visual tracking using discriminative stable regions and K-means clustering
    Zhang, Can-Long
    Jing, Zhong-Liang
    Pan, Han
    Jin, Bo
    Li, Zhi-Xin
    NEUROCOMPUTING, 2013, 111 : 131 - 143
  • [25] A Clustering Method Based on K-Means Algorithm
    Li, Youguo
    Wu, Haiyan
    INTERNATIONAL CONFERENCE ON SOLID STATE DEVICES AND MATERIALS SCIENCE, 2012, 25 : 1104 - 1109
  • [26] A deterministic method for initializing K-means clustering
    Su, T
    Dy, J
    ICTAI 2004: 16TH IEEE INTERNATIONALCONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2004, : 784 - 786
  • [27] A neighborhood granular K-means clustering method
    Chen Y.-M.
    Cai G.-Q.
    Lu J.-W.
    Zeng N.-F.
    Kongzhi yu Juece/Control and Decision, 2023, 38 (03): : 857 - 864
  • [28] A novel method for K-means clustering algorithm
    Zhao, Jinguo, 1600, Transport and Telecommunication Institute, Lomonosova street 1, Riga, LV-1019, Latvia (18):
  • [29] Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm
    Shi Na
    Liu Xumin
    Guan Yong
    2010 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY AND SECURITY INFORMATICS (IITSI 2010), 2010, : 63 - 67
  • [30] Data Analysis of Educational Evaluation Using K-Means Clustering Method
    Liu, Rui
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022