The spatial variability in any kind of geomorphic studies based on terrain attributes are the most important issues. This terrain attributes and their respective characteristics play a significant role in the formation and expansion of ephemeral gullies. Therefore, nowadays, the accuracy of terrain based geomorphic studies has been mostly dependent on the resolution and quality of the DEM (digital elevation model) data. As the rate of erosional power of water flow is dependent on the terrain characteristics, therefore the extraction of several terrain features from DEM data is necessary in the study of gully erosion. This case study investigates the scale-dependence of DEM-derived terrain factors in gully erosion susceptibility (GES). This work on Garhbeta-I C.D. Block has focused on the comparison among the predicted GES maps through five types of DEM i.e. Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Cartosat-1, ALOS World 3D-30 m (AW3D30) and Advanced Land Observation satellite (ALOS) coupled with the machine learning modelling approach of artificial neural network (ANN), convolution neural network (CNN) and deep learning neural network (DLNN) algorithm. A total of sixteen conditioning factors were chosen for GES assessment based on the topographical, hydro-climatological conditions and multi-collinearity analysis. Here, importance variables are measured by mean decrease accuracy (MDA) method of random forest (RF) algorithm and the result is shown that elevation is the most important factor for gully occurrences. Validation result of receiver operating characteristics-area under curve (ROC-AUC) has been indicates that DLNN model in ALOS DEM (AUC = 0.958) gives the most optimal accuracy in GES assessment. The output maps can assist in identifying gully-prone risk areas, and several suitable with sustainable managements should be taken for conservation accordingly.

任何基于地形属性的地貌研究中的空间变异性都是最重要的问题。这种地形属性及其各自的特征对短暂性沟壑的形成和扩展起着重要作用。因此，如今，基于地形的地貌研究的准确性主要取决于 DEM（数字高程模型）数据的分辨率和质量。由于水流侵蚀力的大小取决于地形特征，因此在研究沟谷侵蚀时，需要从DEM数据中提取多种地形特征。本案例研究调查了 DEM 衍生的地形因素在沟壑侵蚀敏感性 (GES) 中的尺度依赖性。Garhbeta-I CD 上的这项工作 Block 通过五种类型的 DEM，即航天飞机雷达地形任务 (SRTM)、先进的星载热发射和反射辐射计 (ASTER)、Cartosat-1、ALOS World 3D-30 m (AW3D30) 和先进陆地观测卫星（ALOS）结合人工神经网络（ANN）、卷积神经网络（CNN）和深度学习神经网络（DLNN）算法的机器学习建模方法。基于地形、水文气候条件和多重共线性分析，总共选择了 16 个条件因子进行 GES 评估。在这里，重要性变量通过随机森林（RF）算法的均值递减精度（MDA）方法进行测量，结果表明海拔是沟壑发生的最重要因素。接受者操作特征曲线下面积 (ROC-AUC) 的验证结果表明，ALOS DEM 中的 DLNN 模型 (AUC = 0.958) 在 GES 评估中提供了最佳的准确性。输出地图可以帮助识别易形成沟壑的风险区域，并且应该采取一些适合可持续管理的方法进行相应的保护。