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.

河道头提取是了解流域水文过程、流域起源、径流生成和景观演变的基础。空间恒定的阈值（例如上坡面积、坡面积和曲率）因其简单高效而被广泛用于通道头提取。然而，很难确定具有不同地形、土壤类型和土地覆盖的流域内和流域之间的阈值。在这项研究中，引入了一种模糊无监督机器学习方法（例如，模糊 c 均值聚类）来确定通道头位置，以避免需要阈值参数。地形属性（例如，上坡面积、坡度、曲率和高程）和坡度区域组合属性（例如，AS ²和 S/ln(A)) 用作输入变量。还根据所提出的方法预测通道水头的能力分析了表面地形分辨率的敏感性。选择了两个带有野外映射河道头的集水区，即俄亥俄州的 Indian Creek 和 Mid Bailey Run，进行调查和比较。所提出的方法在定位通道头方面表现良好。从所提出的方法中识别出的通道头的准确性可与已发布的最先进的通道提取方法相媲美。同时，所提出的方法具有较低的计算负担。我们的研究结果还揭示了地形属性在定位渠道头方面的综合影响。