scholarly journals Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 781 ◽  
Author(s):  
Ayesha S Rahman ◽  
Ataur Rahman
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
New South ◽  
Principal Component ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
South Wales ◽  
Regional Flood Frequency Analysis ◽  
And Cluster Analysis ◽  
Regional Flood Frequency

This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/group formed with smaller sized catchments performs better (with a median relative error values ranging from 22% to 37%) than other clusters/groups. No linkage is found between the degree of heterogeneity in the clusters/groups and precision of flood quantile prediction by the multiple linear regression technique.

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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