Channel head extraction based on fuzzy unsupervised machine learning method.

Channel head extraction is fundamental to understand the catchment hydrological processes, catchment origin, runoff generation and landscape evolution. A spatially constant threshold value such as upslope area, slope-area, and curvature have been widely used for channel head extraction because of their simplicity and efficiency. However, it is very difficult to determine the threshold values within and between catchments with different topography, soil types, and land cover. In this study, a fuzzy unsupervised machine learning method (e.g., the fuzzy c-means clustering) is introduced to determine the channel head locations to avoid the need for a threshold parameter. The topographic attributes (e.g., upslope area, slope, curvature, and elevation) and slope-area combined attributes (e.g., AS2 and S/ln(A)) are used as input variables. The sensitivity of surface terrain resolution is also analyzed in terms of the proposed method's ability to predict channel heads. Two catchments with field mapped channel heads, namely Indian Creek and Mid Bailey Run in Ohio, are selected for investigation and comparison. The proposed method performs well in terms of locating the channel head. The accuracy of identified channel heads from the proposed method is comparable to published state-of-the-art channel extraction methods. Meanwhile, the proposed method has a low computational burden. Our findings also reveal the comprehensive impact of topographic attributes in locating channel heads. • An unsupervised machine learning method used for channel head extraction is proposed. • The channel heads are extracted based on the characteristic difference of underlying surface. • The channel heads are independently identified within each tributary. [ABSTRACT FROM AUTHOR]