scholarly journals Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space

2007 ◽  
Vol 43 (7) ◽  
Author(s):  
C. Shu ◽  
T. B. M. J. Ouarda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Frequency Analysis ◽  
Canonical Correlation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Ungauged Sites ◽  
Artificial Neural

scholarly journals A hybrid regionalization method based on canonical correlation analysis and cluster analysis: a case study in northern Iran

2019 ◽  
Vol 50 (4) ◽  
pp. 1076-1095
Author(s):  
Ali Ahani ◽  
S. Saeid Mousavi Nadoushani ◽  
Ali Moridi
Keyword(s):  
Cluster Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Clustering Algorithm ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Northern Iran ◽  
Regional Flood Frequency Analysis ◽  
And Cluster Analysis

Abstract The performance of regionalization methods used for regional flood frequency analysis is affected considerably by the features used to identify the homogeneous regions (e.g., climatological, meteorological, geomorphological, and physiographic characteristics of the watersheds). In this study, a regionalization method is proposed that takes advantage of the two widely used techniques in regionalization of watersheds: canonical correlation analysis and cluster analysis. In the proposed method, the canonical correlation analysis is utilized to select or weight features that then will be used by a hybrid clustering algorithm for regionalization of watersheds. The proposed method is applied to Sefidrud basin, located in the north of Iran, to implement regionalization with two, three, four, and five regions. Performance assessment of the proposed method shows that all the options of the proposed method can be effective alternatives to some common regionalization methods to improve the homogeneity of the regions. The results indicate that the method can satisfy the homogeneity conditions approximately for all the regions which were identified in the study area.

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