A fuzzy hybrid clustering method for identifying hydrologic homogeneous regions

Abstract

Identification of hydrologic homogeneous regions (HHR) facilitates prioritization of watershed management measures. In this study, a new methodology involving a combination of self-organizing features maps (SOFM) method and fuzzy C-means algorithm (FCM), designated as SOMFCM, is presented to identify HHRs. The case study region is Walnut Gulch Experimental Watershed (WGEW) located in Arizona. The input data consisted of a number of factors that influence runoff generation processes, including ten surface features as well as various rainfall values corresponding to 25, 50, and 100 years return periods. Factor analysis (FA) was applied for the selection of effective surface features along with rainfall value, used in the clustering algorithm. Validation procedure indicated that the best clustering scenario was achieved through merging three layers including TPI (topographic position index), CN (curve number), and P50 (50-year rainfall). The optimum number of clusters turned out to be six while the fuzzification parameter became 1.6. The presented methodology may be proposed as a simple approach for identifying HHRs.

Identification of hyporheic extent and functional zonation during seasonal streamflow recession by unsupervised clustering of time‐lapse electrical resistivity models

“…Another intriguing potential extension exists around supervised clustering or similar machine‐learning techniques in which cluster characteristics are defined based on an initial training dataset containing time‐lapse ER and more easily obtained ancillary measurements to allow for prediction elsewhere. Such methods have been applied to classify and then predict spatiotemporal evolution of other hydrologic patterns such as seasonal soil moisture (Hermes et al, 2020) and hydrologically homogeneous regions within catchments (Nadoushani et al, 2018) based on topographic indices, but not, to our knowledge, for hyporheic exchange. For instance, exploring whether high‐resolution topography data from within the river corridor (rather than the whole catchment) could be used to predict hyporheic zonation patterns beyond discrete transects is an intriguing possibility.…”

Influence of Terrain Factors on Urban Pluvial Flooding Characteristics: A Case Study of a Small Watershed in Guangzhou, China

“…Referring to the method of micro terrain classification in ecology, R4.2.1 was used to conduct C-means fuzzy cluster analysis for the terrain parameters of each sample in the study area [72,73].…”

Identification of Homogeneous Regions of Specific Minimum Flows in the State of Goiás, Brazil

“…According to Dikbas et al (2013) the method can be successfully applied in the classification of maximum annual flows and in the identification of hydrologically homogeneous regions. On the basis of homogeneous regions, it is possible to transfer data from one location to another, where these do not exist or are scarce, facilitating management in watersheds without monitoring (Nadoushani et al, 2018). Therefore, the present paper aimed to fill the gaps in different watershed with low or none, fluviometric stations, through the determination of homogeneous regions of minimum flows, using the k-means cluster analysis methodology, enable a better management of water resources in the state of Goiás in Brazil.…”