Comparison of Ordinary and Generalised Least Squares Regression Models in Regional Flood Frequency Analysis: A Case Study for New South Wales

2011 ◽  
Vol 15 (1) ◽  
pp. 59-70 ◽  
Author(s):  
K Haddad ◽  
A Rahman ◽  
G Kuczera
Keyword(s):  
Frequency Analysis ◽  
Regression Models ◽  
New South ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Least Squares Regression ◽  
South Wales ◽  
Regional Flood Frequency Analysis ◽  
Regional Flood Frequency

Regional Flood Frequency Analysis Based on L-Moment Approach (Case Study Tisza River Basin)

2019 ◽  
Vol 46 (6) ◽  
pp. 853-860 ◽  
Author(s):  
Igor Leščešen ◽  
Marko Urošev ◽  
Dragan Dolinaj ◽  
Milana Pantelić ◽  
Tamás Telbisz ◽  
...  
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Regional Flood Frequency Analysis ◽  
Tisza River ◽  
Moment Approach ◽  
Regional Flood Frequency

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.

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