A feature weighting and selection method for improving the homogeneity of regions in regionalization of watersheds

Abstract

The method used for feature selection or feature weighting in regionalization of watersheds may affect the results of regionalization methods considerably. It can play a key role in forming hydrologically homogeneous regions for regional flood frequency analysis. In this study, a method based on exploring the nearest and farthest neighbours of data points is introduced for identifying salient features for regionalization of watersheds. The method includes options to relate watershed features to flood data records in order to increase the homogeneity of the regions. The nearest and farthest neighbours are identified based on the criteria such as the mutual information criterion and Spearman's rank correlation coefficient. Then, the watershed features more able to explain the relationships between the nearest and farthest neighbours are identified as salient features to form homogeneous features for regional flood frequency analysis. The results show that the optimum option of the proposed method improves the performances of the hard and fuzzy clustering algorithms in more than half of the cases based on the cluster validity indices. Furthermore, the results reveal that the optimum option can increase the number of the homogeneous regions formed by clustering algorithms to a great extent. By using the optimum option with 5 nearest and 5 farthest neighbours, longitude, drainage area, and run‐off coefficient are identified as the salient features to regionalize Sefidrud basin. The results show that the proposed method can be considered as an efficient method to form homogeneous regions for regional flood frequency analysis.

Exponent determination in a poorly gauged basin system in Nigeria based on flow characteristics investigation and regionalization method

“…Therefore, there is need to exploit other means of predicting flow characteristics at ungauged or poorly gauged sites to engender optimal catchment harnessing plan by extrapolating or transferring data from gauged to ungauged sites, a process called regionalization [8]. However, watershed regionalization is of two types [9]: regionalization based on catchment attributes as presented in [10][11][12] and regionalization based on spatial proximity as reviewed in [16][17][18][19][20][21][22]. This paper's regionalization method is that of the former, which involves the formulation of an observational relationship between discharge and catchment-area characteristics explicitly [13,14].…”

“…Results obtained show the technique can fulfill the homogeneity conditions for all the regions identified in the study area. [21] explore the method of the nearest and farthest neighbor data points to identify relevant features using criteria such as Spearman's rank correlation coefficient and mutual information criterion needed for watershed regionalization by relating catchment features to flood data records to improve regions homogeneity. The study identified drainage area, longitude, and runoff coefficient as prominent characteristics in Sefidrud basin regionalization utilizing the best choice of five farthest and five nearest.…”