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